The module handbook is the official guide for students regarding their academic progress and program of study. It describes the modules and the corrsponding exams. It also explains their interdependencies, for example the need to pass one module before taking another. For each module its content and the qualification goals are provided. Furthermore, the module handbook explains the means of measuring academic success, such as the type of exam or course work (in German: "Studienleistung") the students must pass. The scope of each module is indicated by credit points (CP), which are credited after the successful completion of the module. CP = ECTS (European Credit Transfer System) = Leistungspunkte in German (LP).
The course catalog (in German: "Vorlesungsverzeichnis", https://campus.studium.kit.edu/events/catalog.php) complements the module handbook and provides up-to-date information on various event data (e.g. time and place of the course) for each semester.
Every semester the module handbook is updated. The new module handbook is available about a month before the semester begins. Prior to that students can refer to the current module handbook but should expect changes, especially with elective modules.
The most important information regarding modules is:
The purpose of this introduction is to give an overview of the study program in computer science at KIT and provide additional regulations, not specified in the Study and Examination Regulations (in German Prüfungsordnung). It provides students with a more well-rounded idea of the requirements of the modules and their field of study. It also gives information on elective modules, study minors and interdisciplinary qualifications (= soft skills), helping the students to make a more personal plan of study, taking into consideration factors like the turns of the modules.
The Master of Science in Computer Science continues the education and development of the scientific competences that the students have acquired during their Bachelor program of study. The Master Program of Study provides students with the knowledge and skills necessary for scientific work and research. The program stands out due to the variety and broad range of the courses. This Master Program of Study is structured to provide well-grounded and a broad based education at the same time. Students are required to take two graduate specializations. The program offers a wide variety of specializations in computer science areas including Theoretical Foundations, Algorithm Engineering, Cryptography and Security, Parallel Computing, Software Engineering and Compiler Construction, Design of Embedded Systems and Computer Architectures, Telematics, Anthropomatics and Cognitive Systems and Robotics and Automation.
The core focus of the program is the two graduate specializations. For each specialization, students have to select courses of at least 15 ECTS. For a comprehensive education the program offers a broad variety of compulsory courses. Students must also select a minor in a related interdisciplinary field (minor studies). Key competences such as social and teamwork skills are also mandatory (key competences).
Graduates of the Master of Science in Computer Science are equipped with essential skills:
Graduates are able to independently apply and enhance their scientific knowledge and methods in computer science. They can assess the relevance and consequences of different computer science methods in solving complex scientific and social problems. Graduates have the necessary skills needed to successfully solve applied as well as scientifically complex problems in the field of computer science and related interdisciplinary fields.
Graduates can present and explain computer science ideas clearly and convincingly, both orally and in writing. They are able to communicate effectively to and with technical and non-technical audiences.
Graduates are able to work in multidisciplinary teams. They have project planning and organizing skills.
Graduates recognize the impact of computer science in a societal context. They have the understanding of professional and ethical responsibilities and are able to act accordingly.
Graduates are able to adapt to the newest technologies and use their knowledge for further development.
The KIT Department of Informatics offers eight different certificates within the Master's program. The Master Computer Science can be completed with or without a certificate. The regulations for completing a certificate are ment as guidelines for structuring the study plans, no additional effort should be necessary. The study and examination regulations for the Master's degree program apply unchanged, when students aim to achieve a certificate. Certificates are awarded at the end of the studies in addition to the Master's certificate in computer science, e.g. “Master of Computer Science with an IT Security profile”. An overview of the certificates and the associated guidelines and regulations can be found at: https://www.informatik.kit.edu/english/9378.php
The Computer Science program has a structure based on modules. A module may consist of several courses or only one course. Modules themselves are classified into ten areas of specialization. The module hand book only contains those areas of specialization, that can be studied completly in english. For further areas of specialization, you may refer to the german module hand book of "Master of Science in Informatik". The structure of the Master Program of Study is:
Further constraints students must fulfill are:
To keep track of the students’ performance, study and examination achievements are evaluated with credit points (CP = ECTS European Credit Transfer System, in German Leistunspunkte = LP). One credit point corresponds to approximately 30 hours of workload for an average student.
Modules contain one or more partial achievements and their description. Partial achievements are abstract descriptions of the examinations or of the type of course work required for passing the module. Prerequisites and recommendations are also specified for each partial achievement.
Modules and partial achievements are assigned credit points. On the one hand the credits indicate the amount of work necessary in order to pass the examinations and fullfill all requirements in a module. On the other hand, the credits usually also indicate the weight of a partial achievement in a specific module. Exceptions from this rule are specified accordingly.Details on the calculation of the module grades or of the Bachelor final grade are published on the department website: https://www.informatik.kit.edu/faq-wiki/doku.php?id=notenberechnung.
The section field of study (see section 3) in the handbook contains the structure of the master program of study and the specific modules to be chosen in each area. From there you can navigate to each module and their partial achievements. The courses are also linked to the partial achievements, making it easier to understand modules.
Modules are dynamic constructs, in which updates and changes can occur on a regular basis. Some modules are no longer offered, some partial achievements and associated courses or prerequisites change (e.g. if the type of the exam changes). The module handbook of the current semester is therefore always binding for students. If the requirements of a module change, students usually may still complete the module in the version they started with (e.g. if they already took an exam). This means, that the exams in the modules can be taken, even if the course is not being offered anymore, provided that students have already begun the module. Changes and associated rules for changing between module versions are usually announced in advance. In case of problems with the online registration for examinations, the Informatics Study Program Service (ISS) (e-mail: beratung-informatik@informatik.kit.edu) can assist. ISS should also be contacted, if a module has been started, but can no longer be completed.
The completed module will appear in the individual study plan (in German Studienablaufplan).
The Master Program of Study in Computer Science is structured for a period of two years with two semesters each. All modules have level four, i.e. master level.
Exams can be written, oral or they can be in form of an examination of another type (in German: Prüfungsleistung anderer Art). These exams are graded. Partial achievements can also be in form of ungraded course work (in German: Studienleistung). For further information, please refer to the Study and Examination Regulations (in German: Studien- und Prüfungsordnung) §4. Each partial achievement is linked to the corresponding courses (exercise, lecture, seminar, practical course etc.) and the exam event (in German Prüfungsveranstaltung, see next section).
Registration and withdraw (or sometimes de-registration, in German: Abmeldung) from exams or course works take place in the Campus-Management Portal (CMS https://campus.studium.kit.edu). Deadlines on registration and withdraw are provided on the website of the course and in CMS. Most courses use the ILIAS E-Learning-Platform at KIT for posting and exchanging information (https://www.zml.kit.edu/lms-ilias.php).
In order to register for an exam (or course work) students have to access the exam event (in German Prüfungsveranstaltung). Students must ensure that they first select the module and the partial achievement in their individual study plan in CMS. An exam event specifies not only the time and place when and where an exam takes place, but also provides students with further information regarding the exam or course work as well as deadlines.
Students are encouraged to verify that they are actually registered for the exam or course work and that the status is registered (in German: angemeldet). If there are concerns regarding the registration, students should contact the ISS (e-mail: beratung-informatik@informatik.kit.edu). Participating in exams without registration is not permitted! Further information regarding registration and withdrwal can be found in https://www.informatik.kit.edu/faq-wiki/doku.php?id=start
Each exam (oral, written or of another type) can usually be repeated once. In the case of a written exam, after failing twice, an oral re-examination takes place promptly (usually in the same examination period). This exam can only be “passed” (4.0) or “not passed” (5.0). If an exam is not passed after the re-examination, students lose their right to study computer science. Participation in further exams is not permitted. There is the possibility to request a second re-examination, which has to be approved by the examination board. If the request is approved, further exams can be taken. However, students will not receive the credits for these exams, until the failed exam has been passed in the second re-examination. The second re-examination consists in the participation in a written exam and if this exam is not passed either, in the participation in a further oral re-examination. If the second re-examination is failed, the credits passed in between are discarded.
Course work can be repeated until they are being passed, if no further regulations are provided in the module handbook.
The Department of Informatics at KIT offers help with questions regarding studies, applications or student organization through the Informatics Study Program Services (ISS) (e-mail: beratung-informatik@informatik.kit.edu). ISS is an official service and provides official information.
The student representative body for informatics (Fachschaft Mathe-Info, FSMI) also gives helpful and in some cases more tailored advice. They offer assistance with queries and give advice regarding your studies.
To complete the masters’ degree in computer science students need to complete 120 CP. The credits are mostly achieved through taking different modules but also through the master thesis (30 CP). Students are allowed to exceed the maximum of 120 CP by one single module. Credits should be spread out evenly over all semesters.
The table in Figure 1 contains an overview of the Master Program of Study.
During the studies at least four advanced mandatory modules (with 6 CP each) must be completed. They can be chosen in one of the specialization areas or in the elective studies area.
There are ten areas of specializations in English. This module handbook only features ten areas of specialization that can be studied completely in English. Further two specialization: Cryptography and System Architecture can be chosen by students with sufficient German language skills.
Students must choose two specilization areas. In each area of specialization students must choose modules of at least 15 CP. 10 CP must be of modules containing lectures. The advanced mandatory modules cannot be chosen to achieve 10 CP on lectures. In the specialization areas Telematics and System Architecture only 8 CP of module containing lectures are required.
A total of overall 73 CP in both specialization areas can not be exceeded.
Once exams have been taken in a specialization area, this specialization area is set. A change of the specialization area is possible with the permission of the examination board. For that students have to fill in a form (for details see https://www.informatik.kit.edu/faq-wiki/doku.php?id=start) and submit it to ISS via E-mail (berating-informatik@informatik.kit.edu). In the section 3 the list of modules for each specialization is listed.
This module handbook contains only modules in Englisch. Students may choose a maximum of 30 CP of the 120 CP in German, if they have the necessary language skills. The geman module handbook should be consulted, in order to know which german modules can be chosen in which areas (spcialization, elective studies etc.) and their specific regulations regarding the exams.
Advanced mandatory modules cover important basic themes in computer science. They assure, that students have a broad education and prepare them for the area of specialization. They are offered once each year. This cannot be guaranteed for other elective modules. Students may also choose advanced mandatory modules in German as part of the four advanced mandatory modules.
Students must take four advanced mandatory modules in their master studies. Advanced mandatory modules, that have been completed during the bachelor studies, cannot be repeated during master studies. The table in Figure 2 provides the list of advanced mandatory modules.
The advanced mandatory modules can be taken either in one of the specialization areas or in the elective studies area.
Students have to take at least 3 CP in seminars. They also have to take at least 6 CP in practical courses. A minimum of 12 CP of seminars and practical courses must be achieved in total. A maximum of 18 CP of seminars and practical courses is permitted.
Seminars and practical courses in minor studies are not subject to these constraints. These constraints regard only seminars and practical courses to be chosen in the areas of specialization and elective studies.
Elective modules are not necessarily offered regularly. The current ones can be found in section 3. All modules of all specialization areas can be chosen in the elective studies area. A maximum of 49 CP is permitted (120 CP minus the minimum of credit points to be achieved in the specialization areas, the minor studies, interdisciplinary qualifications and the master thesis).
To give the students a broader education, minor studies provide knowledge of an adjacent field of study. Students must choose at least 9 CP and a maximum of 18 CP. Additional credits will be discarded. Minor Studies have a significant importance for the future career, to have learned of other fields outside of the computer science core. The minor modules are listed in section 3. Some minor studies have only one module, others consist of several. Students can only choose one minor subject.
Students may also choose an individual compiled list of modules for their minor. The constraints can be read in the FAQ: https://www.informatik.kit.edu/faq-wiki/doku.php?id=ergaenzungsfach.
Another part of the studies are the interdisciplinary qualifications (2 – 6 CP). This area includes key competences and soft skills on social topics, interdisciplinary academic topics as well as foreign languages.
All courses from the House of Competence (HoC), FORUM Science and Society (except computer science or minor studies courses) and from the Language Center (SpZ), but also special courses of the informatics department, can be validated in this area. The courses are not listed in the module handbook, but can be found on the webpages of these institutions. In order to be accepted, English language certificates must demonstrate a level above B2.
These qualifications are not graded. Although some exams are graded and the partial achievement may also be listed in the individual study plan with a grade, the interdisciplinary qualifications can only be passed/failed, so these grades does no contribute to the overall grade of the Master program.
Participation certificats (in German: Teilnahmebescheinigungen) cannot not be validated as interdisciplinary qualifications. An exam or coursework must be done.
During the master studies, students may take additional courses (max. 30 CP). These achievements are not included in the overall grade. If the corresponding partial achievements cannot be selected in the individual study plan, students should contact ISS.
Please note that two specialization subjects and one minor subject must be taken in the Master's program. You can select these in your study plan when clicking on the elective button "Select areas" next to the study program identifier Informatics / Computer Science Master 2025.
| Mandatory | |
|---|---|
| Master‘s Thesis | 30 |
| Areas of Specialization (Elective: 2 items) | |
| Area of Specialization: Algorithm Engineering | 15-73 |
| Area of Specialization: Cryptography and Security | 15-73 |
| Area of Specialization: Data Science | 15-73 |
| Area of Specialization: Design of Embedded Systems and Computer Architectures | 15-73 |
| Area of Specialization: Human-centred Machine Intelligence | 15-73 |
| Area of Specialization: Robotics and Automation | 15-73 |
| Area of Specialization: Software Engineering and Compiler Construction | 15-73 |
| Area of Specialization: Telematics | 15-73 |
| Area of Specialization: Theoretical Foundations | 15-73 |
| Mandatory | |
| Elective Studies in Informatics | 6-49 |
| Minor Studies (Elective: 1 item) | |
|
Minor Studies: Biologically Inspired Robotics
First usage possible from Apr 01, 2026.
|
9-18 |
| Minor Studies: Electrical Engineering | 9-18 |
| Minor Studies: Mathematics | 9-18 |
| Minor Studies: Economics | 9-18 |
| Minor Studies: Law | 9-18 |
| Mandatory | |
| Interdisciplinary Qualifications | 2-6 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Mandatory | ||||
| M-INFO-106828 | Master's Thesis | EN | WT+ST | 30 |
Specialization Coordinator: Prof. P. Sanders
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
Specialization Coordinator: Prof. J. Müller-Quade
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
Specialization Coordinators: Prof. K. Böhm, Prof. G. Neumann
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules: Data Science (Elective: between 15 and 73 credits) | ||||
| M-INFO-106812 | Advanced Bayesian Data Analysis | EN | WT | 5 |
| M-INFO-106655 | Data Science and Artificial Intelligence for Energy Systems | EN | ST | 6 |
| M-INFO-106302 |
Explainable Artificial Intelligence
First usage possible from Oct 01, 2025.
|
EN | ST | 3 |
| M-INFO-106959 | Machine Learning for Natural Sciences | EN | ST | 6 |
| M-INFO-106470 | Machine Learning in Climate and Environmental Sciences | EN | ST | 6 |
| M-INFO-107831 |
Practice of Research (Project, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107832 |
Practice of Research (Project, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
Specialization Coordinators: Prof. J. Henkel, Prof. W. Karl
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules: Design of Embedded Systems and Computer Architectures (Elective: between 15 and 73 credits) | ||||
| M-INFO-107230 | Design and Architectures of Embedded Systems (ESII) | EN | WT | 3 |
| M-INFO-105775 | Embedded Machine Learning Lab | EN | WT+ST | 4 |
| M-INFO-107448 | Hardware-efficient Artificial Intelligence | EN | WT | 3 |
| M-INFO-100807 | Low Power Design | EN | ST | 3 |
| M-INFO-107229 | Optimization and Synthesis of Embedded Systems (ESI) | EN | WT | 3 |
| M-INFO-103706 | Practical Course: Internet of Things (IoT) | EN | WT+ST | 4 |
| M-INFO-107265 | Practical Course: Chip Design I | EN | WT+ST | 3 |
| M-INFO-107266 | Practical Course: Chip Design II | EN | WT+ST | 3 |
| M-INFO-102661 | Practical Course: FPGA Programming | EN | WT+ST | 3 |
| M-INFO-104031 | Practical Course: Low Power Design and Embedded Systems | EN | ST | 4 |
| M-INFO-106503 |
Practical Course: Neural Network Acceleration on FPGAs
First usage possible from Apr 01, 2026.
|
EN | WT+ST | 3 |
| M-INFO-107241 | Practical Introduction to Hardware Security | EN | WT+ST | 6 |
| M-INFO-107831 |
Practice of Research (Project, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107832 |
Practice of Research (Project, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-100850 | Reliable Computing I | EN | Irreg. | 3 |
| M-INFO-102662 | Seminar Dependable Computing | EN | WT+ST | 3 |
| M-INFO-107231 | Seminar: Embedded Systems I | EN | WT+ST | 3 |
| M-INFO-107232 | Seminar: Embedded Systems II | EN | WT+ST | 3 |
| M-INFO-100851 | Testing Digital Systems I | EN | Irreg. | 3 |
| M-INFO-102962 | Testing Digital Systems II | EN | Irreg. | 3 |
| M-INFO-107897 |
Practical Course: Customized Embedded Processor Design
First usage possible from Jul 13, 2026.
|
EN | ST | 4 |
Specialization Coordinator: Prof. R. Stiefelhagen
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
Specialization Coordinator: Prof. T. Asfour
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules: Robotics and Automation (Elective: between 15 and 73 credits) | ||||
| M-INFO-106608 | Autonomous Learning for Intelligent Robot Perception | EN | WT | 4 |
| M-INFO-106190 | Computational Imaging | EN | WT | 5 |
| M-INFO-106650 | HRI and Social Robotics | EN | ST | 6 |
| M-INFO-106649 | Humanoid Robots - Locomotion and Whole-Body Control | EN | ST | 6 |
| M-INFO-107152 | Humanoid Robots - Seminar | EN | WT | 3 |
| M-INFO-107663 |
Localization of Mobile Agents
First usage possible from Apr 01, 2026.
|
EN | ST | 6 |
| M-INFO-106959 | Machine Learning for Natural Sciences | EN | ST | 6 |
| M-INFO-106646 |
Practical Course: Human-Centred Robotics
First usage possible from Oct 01, 2025.
|
EN | WT+ST | 6 |
| M-INFO-107845 |
Practical Course: Machine Learning and Intelligent Systems
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 8 |
| M-INFO-107609 |
Practical Course: Mathematical and Computational Methods in Robotics and AI
First usage possible from Oct 01, 2025.
|
EN | WT+ST | 6 |
| M-INFO-106648 | Practical Course: Movement and Technology | EN | ST | 6 |
| M-INFO-107831 |
Practice of Research (Project, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107832 |
Practice of Research (Project, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107728 |
Research Practical Course on Behavior Foundation Models
First usage possible from Apr 01, 2026.
|
EN | ST | 6 |
| M-INFO-107174 | Research Project Deep Learning for Robotics | EN | WT | 6 |
| M-INFO-107155 | Robotics - Practical Course | EN | ST | 6 |
| M-INFO-107162 | Robotics I - Introduction to Robotics | EN | WT | 6 |
| M-INFO-107123 | Robotics II - Humanoid Robotics | EN | ST | 3 |
| M-INFO-107130 | Robotics III - Sensors and Perception in Robotics | EN | ST | 3 |
| M-INFO-107598 | Robotics Institute Germany - Lecture Series | EN | WT | 3 |
| M-INFO-107090 | Sampling Methods for Machine Learning | EN | ST | 6 |
| M-INFO-107846 |
Seminar for Practical Course: Machine Learning and Intelligent Systems
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 3 |
| M-INFO-107729 |
Seminar on Behavior Foundation Models
First usage possible from Apr 01, 2026.
|
EN | ST | 3 |
| M-INFO-107175 | Seminar: Deep Learning for Robotics | EN | WT | 3 |
| M-INFO-106927 |
Seminar: Exoskeletons & Motion Capture
First usage possible from Oct 01, 2025.
|
EN | WT | 3 |
| M-INFO-106651 | Seminar: Exploring Robotics - Insights from Science Fiction, Research and Society | EN | ST | 3 |
| M-INFO-107436 | Seminar: Failing Forward: Robotics, Critical Reading and the Making of Good Research | EN | WT | 3 |
| M-INFO-107926 |
Seminar: Robot Reinforcement Learning
First usage possible from Jul 29, 2026.
|
EN | Irreg. | 3 |
| M-INFO-107847 |
Stochastic Information Processing
First usage possible from Oct 01, 2026.
|
EN | WT | 6 |
| M-INFO-107834 |
User Studies in Human-Robot Interaction
First usage possible from Oct 01, 2026.
|
EN | WT | 6 |
| M-INFO-107113 | Wearable Robotic Technologies | EN | ST | 4 |
Specialization Coordinators: Prof. A. Koziolek, Prof. R. Reussner
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules: Software Engineering and Compiler Construction (Elective: between 15 and 73 credits) | ||||
| M-INFO-106966 | Compiler Design | EN | Irreg. | 9 |
| M-INFO-107685 |
Empirical Software Engineering
First usage possible from Apr 01, 2026.
|
EN | Irreg. | 4 |
| M-INFO-106626 | Engineering Self-Adaptive Systems | EN | WT | 3 |
| M-INFO-107906 |
Logic Programming
First usage possible from Oct 01, 2026.
|
EN | Irreg. | 6 |
| M-INFO-106102 | Logical Foundations of Cyber-Physical Systems | EN | WT | 6 |
| M-INFO-106931 | Model-Driven Software Development | EN | WT | 3 |
| M-INFO-107739 |
Practical Course: Advanced Software Development Tools
First usage possible from Apr 01, 2026.
|
EN | WT+ST | 6 |
| M-INFO-107677 |
Practical Course: Continuous Software Engineering
First usage possible from Apr 01, 2026.
|
EN | WT+ST | 6 |
| M-INFO-107678 |
Practical Course: Engineering Approaches to Software Development
First usage possible from Apr 01, 2026.
|
EN | WT+ST | 6 |
| M-INFO-106932 | Practical Course: Model-Driven Software Development | EN | ST | 6 |
| M-INFO-107865 |
Practical Course: Performance Engineering in C++ and Rust
First usage possible from Oct 01, 2026.
|
EN | Irreg. | 6 |
| M-INFO-108020 | Practical Course: Programming Language Design and Implementation | EN | Irreg. | 6 |
| M-INFO-107831 |
Practice of Research (Project, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107832 |
Practice of Research (Project, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-105309 | Seminar: Continuous Software Engineering | EN | ST | 4 |
| M-INFO-107236 | Seminar: Software Architecture, Security and Privacy | EN | Irreg. | 4 |
| M-INFO-107625 |
Seminar: Type Systems and Static Program Analysis
First usage possible from Oct 01, 2025.
|
EN | WT | 4 |
| M-INFO-107237 | Software Architecture and Quality | EN | WT | 3 |
| M-INFO-107235 | Software Engineering II | EN | ST | 6 |
| M-INFO-107212 | Software Product Line Engineering | EN | ST | 3 |
| M-INFO-106344 | Software Security Engineering | EN | ST | 3 |
| M-INFO-100719 |
Software-Evolution
First usage possible from Jan 16, 2026.
|
EN | WT | 3 |
| M-INFO-107239 | Software Test and Quality Management (SQM) | EN | WT | 5 |
| M-INFO-107664 | Type Systems and Program Analysis | EN | Irreg. | 6 |
Specialization Coordinators: Prof. S. Abeck, Prof. H. Hartenstein, Prof. M. Ziterbart
Students must choose at least 8 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
Specialization Coordinators: Prof. P. Sanders, Prof. S. Erdweg
Students must choose at least 10 CP of lectures (no practical courses, no seminars, no advanced mandatory courses). In total students must choose at least 15 CP in each specialization.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules: Theoretical Foundations (Elective: between 15 and 73 credits) | ||||
| M-INFO-107200 | Advanced Data Structures | EN | ST | 5 |
| M-INFO-106960 | Algorithmic Graph Theory | EN | Irreg. | 5 |
| M-INFO-106961 | Algorithms for Visualization of Graphs | EN | Irreg. | 5 |
| M-INFO-107201 | Algorithms II | EN | WT | 6 |
| M-INFO-104447 | Automated Planning and Scheduling | EN | Irreg. | 5 |
| M-INFO-107599 | Beating the Worst-Case in Practice: Unexpectedly Efficient Algorithms | EN | Irreg. | 6 |
| M-INFO-107228 | Computational Geometry | EN | Irreg. | 6 |
| M-INFO-106256 | Constructive Logic | EN | ST | 5 |
| M-INFO-106644 | Fine-Grained Complexity Theory & Algorithms | EN | Irreg. | 6 |
| M-INFO-107211 | Graph Partitioning and Graph Clustering in Theory and Practice | EN | Irreg. | 5 |
| M-INFO-107906 |
Logic Programming
First usage possible from Oct 01, 2026.
|
EN | Irreg. | 6 |
| M-INFO-106102 | Logical Foundations of Cyber-Physical Systems | EN | WT | 6 |
| M-INFO-107167 | Parameterized Algorithms | EN | Irreg. | 6 |
| M-INFO-106784 | Practical Course: Fine-grained Algorithm Design and Engineering | EN | Irreg. | 6 |
| M-INFO-107494 | Practical Course: Interactive Theorem Proving | EN | Irreg. | 3 |
| M-INFO-107845 |
Practical Course: Machine Learning and Intelligent Systems
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 8 |
| M-INFO-107238 | Practical SAT Solving | EN | Irreg. | 5 |
| M-INFO-107831 |
Practice of Research (Project, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107832 |
Practice of Research (Project, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 10 |
| M-INFO-107168 | Probability and Computing | EN | Irreg. | 5 |
| M-INFO-107209 | Seminar: Advanced Topics on SAT Solving | EN | WT | 3 |
| M-INFO-106086 | Seminar: Algorithm Engineering | EN | Irreg. | 4 |
| M-INFO-107027 | Seminar: Current Trends in Theoretical Computer Science | EN | Irreg. | 4 |
| M-INFO-106645 | Seminar: Fine-Grained Complexity Theory & Algorithms | EN | Irreg. | 4 |
| M-INFO-103306 | Seminar: Proofs from THE BOOK | EN | Irreg. | 3 |
| M-INFO-107172 | Seminar: Recent Highlights in Algorithms | EN | Irreg. | 4 |
| M-INFO-107625 |
Seminar: Type Systems and Static Program Analysis
First usage possible from Oct 01, 2025.
|
EN | WT | 4 |
| M-INFO-107639 |
Theory of Satisfiability Algorithms
First usage possible from Apr 01, 2026.
|
EN | Irreg. | 4 |
| M-INFO-107664 | Type Systems and Program Analysis | EN | Irreg. | 6 |
| M-INFO-107846 |
Seminar for Practical Course: Machine Learning and Intelligent Systems
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 3 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules (Elective: between 9 and 18 credits) | ||||
| M-MACH-107806 |
Autonomous Driving in Practice
First usage possible from Oct 01, 2026.
|
EN | WT | 6 |
| M-MACH-106903 | Biologically Inspired Robots | EN | ST | 3 |
| M-MACH-106905 | CAD Engineering Project for Intelligent Systems | EN | WT+ST | 6 |
| M-MACH-106904 | Practical Course: Software Development and Application of Mobile, Bio-Inspired Robots | EN | WT | 6 |
| M-MACH-107621 |
Robotic Intelligence: Navigation and Mobile Manipulation
First usage possible from Apr 01, 2026.
|
EN | ST | 6 |
| M-MACH-107558 |
Robotic Intelligence: Perception and Scene Understanding
First usage possible from Apr 01, 2026.
|
EN | WT | 6 |
| M-MACH-106902 | Seminar: Bionic Algorithms and Robot Technologies | EN | WT+ST | 3 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules (Elective: between 9 and 18 credits) | ||||
| M-ETIT-101834 |
Business Innovation in Optics and Photonics
First usage possible from Oct 01, 2025.
|
EN | WT | 4 |
| M-ETIT-105616 | Channel Coding: Algebraic Methods for Communications and Storage | EN | ST | 3 |
| M-ETIT-105617 | Channel Coding: Graph-Based Codes | EN | WT | 6 |
| M-ETIT-100449 | Hardware Modeling and Simulation | EN | WT | 4 |
| M-ETIT-106963 | Hardware Synthesis and Optimization | EN | ST | 6 |
| M-ETIT-105971 | Mobile Communications | EN | WT | 4 |
| M-ETIT-105604 | Nano- and Quantum Electronics | EN | ST | 6 |
| M-ETIT-100456 | Optical Engineering | EN | WT | 4 |
| M-ETIT-107839 |
Seminar Radiation Protection in Medicine
First usage possible from Oct 01, 2026.
|
EN | WT | 3 |
| M-ETIT-105073 | Student Innovation Lab | EN | WT+ST | 15 |
| M-ETIT-100537 | Systems and Software Engineering | EN | WT | 6 |
| M-ETIT-100462 | Systems Engineering for Automotive Electronics | EN | ST | 4 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules (Elective: between 9 and 18 credits) | ||||
| M-MATH-101336 | Graph Theory | EN | see notes | 9 |
| M-MATH-102950 | Combinatorics | EN | see notes | 9 |
| M-MATH-106957 | Modern Methods in Combinatorics | EN | Irreg. | 6 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules (Elective: between 9 and 18 credits) | ||||
| M-WIWI-105659 | Advanced Machine Learning and Data Science | EN | WT+ST | 9 |
| M-WIWI-106258 | Digital Marketing | EN | WT+ST | 9 |
| M-WIWI-101503 | Service Design Thinking | EN | Irreg. | 9 |
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Elective Modules (Elective: between 9 and 18 credits) | ||||
| M-INFO-107030 | EU Data Protection Law | EN | WT | 3 |
| M-INFO-107029 | Public International Law with an Economic Law Focus | EN | ST | 3 |
| M-INFO-107028 | Seminar: Law and Legal Studies | EN | WT+ST | 3 |
| M-INFO-108019 | EU-Law | EN | WT | 3 |
All courses from the House of Competence (HoC), FORUM Science and Society (except computer science or minor studies courses) and from the Language Center (SpZ), but also special courses of the informatics department, can be validated in this area. The courses are not listed in the module handbook, but can be found on the webpages of these institutions. In order to be accepted, English language certificates must demonstrate a level above B2.
All courses from the House of Competence (HoC), FORUM Science and Society (except computer science or minor studies courses) and from the Language Center (SpZ), but also special courses of the informatics department, can be validated in this area. The courses are not listed in the module handbook, but can be found on the webpages of these institutions. In order to be accepted, English language certificates must demonstrate a level above B2.
| ID | Name | Language | Term offered | Credits |
|---|---|---|---|---|
| Interdisciplinary Qualifications (Elective: between 2 and 6 credits) | ||||
| M-INFO-107254 | Interdisciplinary Qualifications | EN | WT+ST | 6 |
| M-INFO-107829 |
Practice of Research (Methods, 1st Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 2 |
| M-INFO-107830 |
Practice of Research (Methods, 2nd Semester)
First usage possible from Oct 01, 2026.
|
EN | WT+ST | 2 |
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112775 | Access Control Systems: Models and Technology | ST | 5 | Hartenstein |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Access control systems are everywhere and the backbone of secure services as they incorporate who is and who is not authorized: think of operating systems, information systems, banking, vehicles, robotics, cryptocurrencies, or decentralized applications as examples. The course starts with current challenges of access control in the era of hyperconnectivity, i.e., in cyber-physical or decentralized systems. Based on the derived needs for next generation access control, we first study how to specify access control and analyze strengths and weaknesses of various approaches. We then focus on up-to-date proposals, like IoT and AI access control. We look at current cryptographic access control aspects, blockchains and cryptocurrencies, and trusted execution environments. We also discuss the ethical dimension of access management. Students prepare for lecture and exercise sessions by studying previously announced literature and by preparation of exercises that are jointly discussed in the sessions.
Lecture workload:
Σ = 150h = 5 ECTS
Basics according to the lectures "Information Security" and "IT Security Management for Networked Systems" are recommended.
| Coordinators: |
Prof. Dr. Nadja Klein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Data Science
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113673 | Advanced Bayesian Data Analysis | WT | 5 | Klein |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
• Develop a deep understanding of Bayesian statistical principles and computational techniques.
• Master the application of Bayesian regression models to real-world data.
• Gain proficiency in Markov Chain Monte Carlo (MCMC) methods, including Metropolis-Hastings and Gibbs sampling.
• Acquire skills in implementing Bayesian models using relevant software tools such Stan.
This course deepens students' understanding of Bayesian methods and introduces the latest advancements in Bayesian computation. It is designed for Master students in Computer Science, Mathematics, Economathematics, Techno-Mathematics, Business Informatics, or similar programs seeking to enhance their expertise.
Examples of topics covered are the review of key Bayesian concepts including Bayes' Theorem, conjugate prior distributions, and posterior inference. For instance, students may explore the Beta-Binomial conjugacy, where a Beta prior pairs with a Binomial likelihood, and the Normal-Normal conjugacy, where a Normal prior pairs with a normal likelihood with known variance. These examples demonstrate how conjugate priors simplify posterior calculations and enhance analytical tractability.
Next, students delve into Bayesian supervised learning, covering linear, logistic, and nonparametric approaches, with an emphasis on applying Bayesian methods to real-world data and interpreting results.
The course also covers ways to perform posterior estimation, such as, Markov Chain Monte Carlo (MCMC) inference, including the Metropolis-Hastings algorithm and Gibbs sampling. We explore Bayesian high-dimensional regression techniques, such as the horseshoe prior, for handling models with many predictors. Additionally, students will learn about mixture models and Dirichlet processes, which are powerful tools for modelling heterogeneous data and uncovering latent structures.
We conclude with approximate inference methods, including variational inference and Approximate Bayesian Computation (ABC), essential for dealing with complex models and large datasets.
Lecture with 2 SWS + 1 SWS exercise
5 ECTS corresponds to approx. 150 hours
approx. 30 hours lecture attendance,
approx. 15 hours exercise attendance,
approx. 80 hours post-processing (self-study)
approx. 25 hours exam preparation (self-study)
- Knowledge in R or Python
- Mathematics-heavy lecture. The basics will be reviewed, but mathematical proficiency is helpful
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
(Usage from 10/1/2026)
Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115279 | Advanced Competitive Programming | Irreg. | 6 | Bläsius, Künnemann-Goos |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
After completion of the module, students are able to:
The lectures present algorithms and data structures that are useful in practical settings (e.g., programming contests) due to their efficiency and comparably short implementations. For each presented topic (including: string algorithms, number theory, graphs, tree data structures, etc.), the students solve practical programming assignments. In addition, the students participate in contests simulating conditions of popular competitive programming contests.
Among others, the course provides an ideal preparation to participate in collegiate programming contests. Attending this course is especially recommended for students who might be interested in participating in the ACM ICPC Northwestern Europe Regional Contest (NWERC).
6 CP corresponds to a workload of roughly 180 hours, distributed as follows:
- attendance of lectures: 30 h
- preparation and review of lecture materials: 30 h
- programming assignments: 100 h
- preparation of exam contest: 20 h
The course assumes basic knowledge of algorithms and data structures (e.g., lectures Algorithms 1 and 2) and coding in C++.
We strongly recommend successful participation in “Basispraktikum zum ICPC Programmierwettbewerb”, a similar course or basic experience with competitive programming contests.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114223 | Advanced Data Structures | ST | 4 | Sanders |
| T-INFO-114224 | Advanced Data Structures Project/Experiment | ST | 1 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students acquire a systematic understanding of algorithmic issues and solution approaches in the area of advanced data structures, building on existing knowledge in the subject area of algorithms. They will also be able to apply learned techniques to related problems and interpret and comprehend current research topics in this area.
Upon successful completion of the course, students will be able to:
• explain terms, structures, basic problem definitions, and algorithms from the lecture;
• select which algorithms and data structures are suitable for solving a problem and, if necessary, adapt them to the requirements of a specific problem;
• use algorithms and data structures, analyze them mathematically, and prove the algorithmic properties.
In this lecture we deal with modern data structures for fundamental objects such as trees, graphs, integers, and strings. These data structures are the basis for many applications and an important part of efficient algorithms. We look at highlights from different research areas and learn techniques for solving a wide variety of problems.
In addition to the theoretical analysis of data structures, we also look at the practical performance of the various data structures and their applications.
The lectures including the project/experiment with 5 CP corresponds to 150 working hours, which are divided approximately as follows:
• ca. 30 hours attending lectures
• ca. 60 hours preparing and following-up lectures
• ca. 30 hours working on the project/experiment
• ca. 30 hours preparing for the examination
| Coordinators: |
Prof. Dr. Maxim Ulrich
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
Minor Studies: Economics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-WIWI-111305 | Advanced Machine Learning and Data Science | WT+ST | 9 | Ulrich |
The assessment is carried out as a single examination of another type. It includes the interim presentations during the project, the quality of the implementation, the written final paper, and the final presentation. All components are integrated into a single overall assessment, and the resulting grade reflects the overall impression of the combined performance.
None
After a successful project, the students can:
The course is targeted at students with a major in Data Science and/or Machine Learning and/or Quantitative Finance. It offers students the opportunity to develop hands-on knowledge on new developments in the intersection of quantitative financial markets, data science and machine learning. The result of the project should not only be a final thesis, but the implementation of methods or development of an algorithm in machine learning and data science. Typically, problems and data are taken from current research and innovations in the field of quantitative asset and risk management.
Total effort for 9 credit points: approx. 270 hours are divided into the following parts: Communication:Exchange during the project: 30 h, Final presentation: 10 h; Implementation and thesis: Preparation before development (Problem analysis and solution design): 70 h, Solution implementation: 110 h, Tests and quality assurance: 50 h.
None
Project
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101332 | Algorithm Engineering | ST | 4 | Sanders |
| T-INFO-111856 | Algorithm Engineering Pass | ST | 1 | Sanders |
See partial achievements (Teilleistung)
There are two partial achievements Algorithm Engineering and Algorithm Engineering Exercises. The partial achievement Algorithm Engineering Exercises must be started to be allowed to take the oral examination for Algorithm Engineering.
See partial achievements (Teilleistung)
The students acquire a systematic understanding of algorithmic problems and solution approaches in the field of Algorithm Engineering, building on existing knowledge in the subject area of algorithms. In addition, they will be able to apply learned techniques to related problems and interpret and comprehend current research topics in the field of Algorithm
Engineering.
Upon successful completion of the course, the student will be able to
• Explain terms, structures, basic problem definitions, and algorithms from the lecture;
• select which algorithms and data structures are suitable for solving an algorithmic problem and, if necessary, adapt them to the requirements of a specific problem;
• Execute algorithms and data structures, analyze them mathematically precise and prove the algorithmic properties;
• Explain machine models from the lecture and analyze algorithms and data structures according to these models
• Analyze new problems from applications, reduce them to their algorithmic core and create a suitable abstract model; based on the concepts and techniques learned in the lecture, design and analyze own solutions in this model, and prove algorithmic properties in this model.
• What is Algorithm Engineering, Motivation etc.
• Realistic modeling of machines and applications
• practice-oriented algorithm design
• implementation techniques
• experimental techniques
• evaluation of measurements
The above skills are taught primarily using concrete examples. In the past these were for example the following topics from the area of basic algorithms and data structures:
• linked lists without special cases
• sorting: parallel, external, superscalar,...
• priority queues (cache efficient,...)
• search trees for integer keys
• Full text indexes
• graph algorithms: minimal spanning trees (external,...), route planning
In each of these cases, the focus is on the best known practical and theoretical methods. These usually differ considerably from the methods taught in beginners' lectures.
Lecture and exercise with a combined 3 semester hours, 5 ECTS
5 ECTS correspond to about 150h of work, split into
about 45h visiting lectures and exercise or block seminar
about 25h preparation and follow-up on lectures
about 40h solving exercise tasks (programming, preparing presentation for mini seminar, etc)
about 40h exam preparation
| Coordinators: |
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113918 | Algorithmic Graph Theory | Irreg. | 5 | Ueckerdt |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know the basic concepts of algorithmic graph theory and the most important graph classes and their characterizations in this context, namely perfect graphs, chordal graphs, comparability graphs, as well as interval, split and permutation graphs. They will also be able to execute and analyze algorithms for recognizing these graphs and for solving basic algorithmic problems on these graphs. They are also able to identify subproblems in applied problems that can be expressed using these graph classes and to develop algorithms for new problems on these graph classes that are related to problems from the lectures.
Many basic problems that arise in many contexts, such as coloring problems or finding independent sets and maximal cliques, are NP-hard in general graphs. However, instances of these difficult problems that occur in applications are often much more structured and can therefore be solved efficiently. The lecture first introduces perfect graphs and their most important subclass, chordal graphs, and presents algorithms for various generally NP-hard problems on chordal graphs. Subsequently, in-depth concepts such as comparability graphs are discussed, with the help of which various other graph classes (interval, split and permutation graphs) can be characterized and recognized, and tools for the design of specialized algorithms for these are presented.
Lecture with 3SWS, 5CP
5 CP corresponds to approx. 150 working hours, of which
approx. 45h lecture attendance
approx. 60 hours of follow-up work and completion of exercises
approx. 45h exam preparation
See partial achievements (Teilleistung)
| Coordinators: |
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113919 | Algorithms for Visualization of Graphs | Irreg. | 5 | Ueckerdt |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students acquire a systematic understanding of algorithmic problems and solution approaches in the field of graph visualization, which builds on existing knowledge in the areas of graph theory and algorithmics.
After successfully completing the course, students will be able to
- explain concepts, structures and basic problem definitions from the lecture;
- execute layout algorithms for different graph classes, analyze them mathematically precisely and prove the algorithmic properties;
- explain complexity results from the lecture and independently perform similar reduction proofs for new layout problems;
- select which algorithms are suitable for solving a given layout problem and, if necessary, adapt them to the requirements of a concrete problem; - select which algorithms are suitable for solving a given layout problem and, if necessary, adapt them to the requirements of a concrete problem. adapt them to the requirements of a specific problem;
- analyze unknown visualization problems from graph drawing applications, reduce them to their algorithmic core and create an abstract model from this; design and analyze their own solutions in this model based on the concepts and techniques learned in the lecture and prove the algorithmic properties.
Networks are relationally structured data that are increasingly appearing in a wide variety of application areas. Examples range from physical networks, such as transportation and supply networks, to abstract networks, such as social networks. Network visualization is a fundamental tool for the investigation and understanding of networks.
Mathematically, networks can be modelled as graphs and the visualization problem can be reduced to the algorithmic core problem of determining a layout of the graph, i.e. suitable node and edge positions in the plane. Depending on the application and graph class, different requirements are placed on the type of drawing and the quality criteria to be optimized. The research field of graph drawing draws on approaches from classical algorithmics, graph theory and algorithmic geometry.
During the course, a representative selection of visualization algorithms will be presented and discussed in depth.
Lecture and exercise with 3 SWS, 5 LP
5 LP corresponds to approx. 150 working hours, of which
approx. 45 hours attendance of the lecture and exercise,
approx. 25 hours preparation and follow-up,
approx. 40 hours working on the exercise sheets
approx. 40 hours exam preparation
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114225 | Algorithms II | WT | 6 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student has an in-depth insight into the theoretical and practical aspects of algorithms and is able to identify and formally formulate algorithmic problems in various application areas. Furthermore, they know advanced algorithms and data structures from the areas of graph algorithms, algorithmic geometry, string matching, algebraic algorithms, combinatorial optimization, and external memory algorithms. They are able to independently understand algorithms they are unfamiliar with, associate them with the above areas, apply them, determine their running time, evaluate them, and select appropriate algorithms for given applications. Furthermore, the student is able to adapt existing algorithms to related problems. In addition to algorithms for concrete problems, the student knows advanced techniques of algorithmic design. This includes parameterized algorithms, approximation algorithms, online algorithms, randomized algorithms, parallel algorithms, linear programming, and algorithm engineering techniques. For given algorithms, the student is able to identify techniques used to better understand these algorithms. In addition, they are able to select appropriate techniques for a given problem and use them to design their own algorithms.
This module is designed to provide students with the basic theoretical and practical aspects of algorithm design, analysis, and engineering. It teaches general methods for designing and analyzing algorithms for basic algorithmic problems, as well as the basic principles of general algorithmic methods such as approximation algorithms, linear programming, randomized algorithms, parallel algorithms, and parameterized algorithms.
Lecture with 3 semester hours + 1 semester hour exercise
6 ECTS correspond to about 180 hours
about 45h visiting the lectures
about 15h visiting the exercises
about 90h follow-up of lectures and solving the exercise sheets
about 30h preparation for the exam
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113668 | Artificial Intelligence & IT-Security | WT | 6 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know basic concepts of applying artificial intelligence and machine learning in computer security, and are able to evaluate the performance, quality, and security of such systems.
• Students know and understand basic concepts of features and feature engineering in computer security as well as basic attacks against learning-based systems.
• Students know how to apply AI in computer security.
• Students are able differentiate attack vectors against AI.
• Students understand limits of learning-based security solutions.
The lecture is about combining the fields of artificial intelligence, machine learning and computer security in practice. Many tasks in the computer security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more “intelligent” security solutions (AI for Security). However, also these learning-based systems can be attacked and need to be secured (Security of AI). As an example, viciously crafted inputs can be exploited by an adversary to cause devastating damage in the application area. It thus is of utmost importance to investigate, research, and know about the security properties of AI methods.
The module introduces students to theoretic and practical aspects of AI in computer security as well as security of AI. We cover basics on features and feature engineering in the security domain, discuss fundamental learning settings in security and point out “Dos and Don’ts” of using AI/ML in computer security. Moreover, we put particular focus on “Explainable AI” (XAI) and it’s use in computer security, before we introduce attacks and defense against learning-based systems as discussed in the first half of the course. We cover input-manipulation attacks (e.g., adversarial examples), model-manipulation attacks (e.g., backdooring attacks), privacy attacks (e.g., model stealing and membership inference) and attacks against XAI.
- 58h attendance time
- 56h preparation and follow-up time
- 66h exam preparation
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-109085 | Automated Planning and Scheduling | Irreg. | 5 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The course offers an introduction to the methods and techniques used in automated planning and scheduling. The course is focused on classical deterministic planning, i.e., planning in a fully observable deterministic environment. The students will learn how to use automated planners and schedulers and also how they work. The topics covered in the lecture include:
2 SWS lecture + 1 SWS exercises
(Preparation and follow-up time: 4h/week for lecture plus 2h/week for exercises; exam preparation: 15h)
Total workload: (2 SWS + 1 SWS + 4 SWS + 2 SWS) x 15h + 15h exam preparation = 9x15h + 15h = 150h = 5 ECTS
| Coordinators: |
Dr.-Ing. Martin Gießler
Kevin Simon
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-115202 | Autonomous Driving in Practice | WT | 6 | Simon |
The final grade is based on: 2 written exams (duration about 30 minutes) and the final presentation of the practical task (duration 30 minutes max.), including the final report (2-3 pages). Submission of the homework is mandatory.
The overall impression will be evaluated.
none
The main objective of the event is the practical implementation of the pipeline required for automated/autonomous driving functions with a real test vehicle. This includes recording the environment using various sensors, processing the recorded sensor data (perception), planning driving maneuver and the final execution of the maneuver by the actuators.
- Sensor data acquisition: Setup and data recording of the sensors on the test vehicle
- Perception: data annotation, segmentation of sensor data, object detection
- Maneuver planning: path and trajectory planning, behaviour generation, etc.
- Maneuver execution: vehicle control, implementation of the driving maneuver in the real test vehicle using actuators
The module grade results of the assessment of the written exams, the presentation, and the final report. Details will be given during the lecture.
Due to capacity limitations, the lecture is limited to 16 students. If necessary, a selection process will be conducted. Places will be allocated based on the students’ academic progress (semester level and subject-specific programming skills).
The workload includes:
Total: 180 h
| Coordinators: |
Prof. Dr. Rudolph Triebel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113327 | Autonomous Learning for Intelligent Robot Perception | WT | 4 | Triebel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are capable of describing the details of different methods for autonomous learning, and they can place them in the context of intelligent robot perception. They are able to derive mathematical principles of these algorithms and they can name and describe relevant applications.
This lecture conveys the main principles of Intelligent Robot Perception, where the major focus is on machine learning techniques that are particularly useful for robot vision applications. The most important design criteria for these methods are run-time and data efficiency, safety, and autonomy, where the latter refers to independence of human interactions and the ability to take decisions during learning (aka. active learning). In the lecture, we will analyse modern learning techniques that meet these criteria, and we will show concrete applications of these in robotic perception tasks such as object detection and pose estimation, grasp detection and semantic mapping.
120h
A basic understanding of probability theory and linear algebra is required
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114898 | Beating the Worst-Case in Practice: Unexpectedly Efficient Algorithms | Irreg. | 6 | Bläsius |
See partial achievements. (Teilleistung)
See partial achievements. (Teilleistung)
The students
• can practically apply the knowledge acquired in the basic modules on algorithm engineering,
• are able to independently produce efficient implementations of algorithmic methods,
• master the methodology for the practical evaluation of algorithms, including the processing, analysis, and interpretation of measurement data,
• possess the ability to communicate the results they obtain.
In addition, participants are able to analyze how various typical properties of instances influence the efficiency of algorithms.
In this lab, students implement and evaluate a set of given algorithmic approaches on their own. The emphasis is on methods that, on real-world instances, perform far better than worst-case theory would suggest. We investigate this gap in performance using empirical methods throughout the course.
Practical course with 4 SWS, 6 LP
6 LP corresponds to approx. 180 working hours, of which
approx. 20 hours attendance time,
approx. 130 hours implementation and evaluation,
approx. 30 hours elaboration and preparation of the presentation
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-113856 | Biologically Inspired Robots | ST | 3 | Rönnau |
See partial achievement
None
Students are familiar with various design principles of the "bionics" method in robotics and can analyze and evaluate models for kinematics, mechanics, control, perception and cognition.
Students understand the lightweight construction concepts and material properties of natural models. They are familiar with the concepts and methods of lightweight robotics and can describe the resulting effects on the energy efficiency of mobile robot systems.
Students can distinguish between different natural muscle types and how they function. They also know the corresponding artificial muscle systems and can derive the underlying muscle model.
Students know the most important human senses and the associated stimulus processing and information coding. They can derive technological sensors that perform the same function in robotics.
Students can differentiate the function of a central pattern generator (CPG) from a reflex. They can theoretically derive neuro-oscillators and explain how they control the movement of a robot. Furthermore, they can generate walking patterns for six-legged robots based on the "Cruse rules".
Students can distinguish between different types of locomotion and suitable stability criteria for walking movements. They know the most important walking patterns for multi-legged walking robots and can represent these in a gait diagram.
Students know the most important algorithms for machine learning methods and can explain their advantages and disadvantages in robotics.
Students know the subsumption system architecture and can evaluate the advantages of a reactive system architecture. They can combine "behaviors" for biologically inspired robots into behavior networks.
Students can apply Mendel's laws and explain the differences between meitosis and mitosis. They can also explain the basic genetic algorithm.
Students can identify the greatest challenges in the development of innovative, humanoid robot systems and are familiar with possible solutions and successful implementations.
The lecture biologically inspired robots deals intensively with robots whose mechanical design, sensor concepts or control architecture were inspired by nature. In detail, we will look at solutions from nature (e.g. lightweight construction concepts using honeycomb structures, human muscles) and then at robot technologies that utilize these principles to solve similar tasks (lightweight 3D printed parts or artificial muscles in robotics).
After discussing these biologically inspired technologies, concrete robotic systems and applications from current research that successfully utilize these technologies will be presented. In particular, multi-legged walking robots, snake-like and humanoid robots are presented and their sensor and drive concepts are discussed.
The lecture focuses on the concepts of control and system architectures (e.g. behavior-based systems) of these robotic systems, with locomotion being the main focus. The lecture ends with an outlook on future developments and the development of commercial applications for these robots.
90 working hours, of which approx:
| Coordinators: |
Prof. Dr. Werner Nahm
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
(Usage from 10/1/2025)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-104572 | Business Innovation in Optics and Photonics | WT | 4 | Nahm |
Type of Examination: examination of another type
Duration of Examination: 4 group presentations à 20 minutes (approx.)
Modality of Exam: The exam consists of four group presentations. 2nd day: Technology Presentation. 3rd day: Development plan presentation. 4th day: Business Canvas presentation. Final presentation: Business pitch. The overall impression is rated.
none
The student has an understanding how innovative concepts for optical and photonics products are transferred into a successful business development. The student knows about and makes first The student develops practical experience of how to analyse, develop, and pitch innovative business ideas based on optics & photonics technologies, specifically in the field of spectroscopy and hyperspectral imaging. Through a series of short lectures followed by extensive group work over the first week of the block course the student acquires hands-on experience of business development as lived in a technology start-up environment. The students are given a basic introduction to spectroscopy and are challenged to propose innovative businesses enabled by evolving technology and market needs. To assist in this process the students are introduced to the innovation methodology “Deep Tech Exploration” and primed on themes from intellectual property management to effective pitching.
The students organize themselves into teams, and work cooperatively within the team to develop and deliver the complete business pitch.
In summary the students:
This course is instructed by ZEISS employees, active within the ZEISS corporate innovation ecosystem.
Monday: Introduction into spectroscopy and the deep tech exploration method
• Technology introduction, state-of-the-art
• Scenario Introduction: Found your own start-up
• Overview Deep Tech Exploration methodology / Select your use case
Tuesday: Deep dive – deep tech exploration
• Methods and ideation techniques
• Fundamental principles in Innovation / Value Proposition
• Define your tech product
Wednesday: Business case development
• How to protect your business idea with IP?
• Develop your business idea with the help of the Business Model Canvas
• Writing a Business plan
Thursday: Storytelling and pitching
• Entrepreneurial fincance: tech product problem sizing / ballparking
• Storytelling
• Preparing an elevator pitch
Friday-Thursday of second week: Final pitch preparation
• Self-organized by groups
Friday of second week: Final pitch
The module grade results of the assessment of the presentations. Details will be given during the lecture.
Total 120 h, thereof 34 h contact hours and 86 h preparation, homework, and self-studies
Good knowledge in optics & photonics. Personal motivation and interest for getting deeper into business development aspects, methods and tools. Commitment to active, regular and coninuous participation in the group work.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-113857 | CAD Engineering Project for Intelligent Systems | WT+ST | 6 | Rönnau |
See module component
None
The module CAD Engineering Project for Intelligent Systems is a project-based master’s-level course that offers students the opportunity to gain and to deepen engineering competencies in the development of intelligent mechatronic systems through practical application. Working in interdisciplinary teams, students conceive, design, and realize a robotic prototype across a complete development cycle.
Starting from a defined problem statement, students develop a technical concept and implement it as a detailed mechanical design using CAD software (Autodesk Fusion 360). Particular emphasis is placed on the basics of rapid prototyping, function-oriented component design, correct parametrization, consideration of manufacturing constraints, and the integration of mechanical and electronic components as well as their software interfacing.
The designed components are predominantly manufactured using additive manufacturing methods (in particular 3D printing) and are supplemented by basic conventional manufacturing techniques. In parallel, students integrate suitable sensors, actuators, and additional electronic components into the system and develop the necessary software for control and regulation of the robotic prototype.
Finally, the developed system is assembled, commissioned, and tested and evaluated under realistic conditions. The results are subsequently documented and presented. In addition to technical expertise, the module particularly promotes project management skills, teamwork, interdisciplinary thinking, and a systematic, solution-oriented approach, as required for the development of intelligent technical systems in research and industry.
Prerequisites:
Basic knowledge of common CAD software solutions, ideally Fusion 360.
Students must hand in a small application, detailing motivation and prior experience. The application is done via Ilias (ilias.studium.kit.edu) - Deadline: 16.04.2026 .
The module grade results of the assessment of the design project, the written assignments and the presentation. Further details will be provided at the beginning of the course.
For further notes see here teaching.imi.kit.edu
Division of content:
| Coordinators: |
Prof. Dr.-Ing. Laurent Schmalen
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-111244 | Channel Coding: Algebraic Methods for Communications and Storage | ST | 3 | Schmalen |
The exam is held as an oral exam of approx. 20 min.
The module grade of the module corresponds to the grade of the oral exam.
The students are able to analyse and assess problems of algebraic channel coding. They can apply methods of algebraic coding theory in the context of communication systems for data transmission and data storage and are able to assess their implementation.Additionally, they will get knowledge to current research topics and research results.
This course focuses on the formal and mathematical basics for the design of coding schemes in digital communication systems. These include schemes for data transmission, data storage and networking. The course starts by introducing he necessary fundamentals of algebra which are then used to derive codes for different applications. Besides codes that are important for data transmission appliations, e.g., BCH and Reed-Solomon-Codes, we also investigate codes for the efficient storage and reconstruction of data in distributed systems (locally repairable codes) and codes that increase the throughput in computer networks (network codes). Real applications are always given to discuss practical aspects and implementations of these coding schemes. Many of these applications are illustrated by example code in software (python/MATLAB).
The module grade of the module corresponds to the grade of the oral exam.
Knowledge of basic engineering as well as basic knowledge of communications engineering.
Previous attendance of the lectures "Communication Engineering I" and "Probability Theory" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Laurent Schmalen
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-111245 | Channel Coding: Graph-Based Codes | WT | 6 | Schmalen |
The success control takes place in the form of an oral examination lasting approx. 25 minutes. Before the examination, there is a preparation phase of 30 minutes in which preparatory tasks are solved.
none
Students will be able to understand and apply advanced and modern methods of channel coding. They get to know various tools of modern coding theory for the analysis and optimization of coding schemes, conceptual design approaches of error correction building blocks as well as applications in digital communications (for example, 5G). Additionally, they will get knowledge to current research topics and research results.
The course expands on the topics dealt with in the lecture “Verfahren der Kanalcodierung”. The focus is on modern methods that have been brought into practice in the past few years and that achieve the capacity limits postulated by Shannon. For this purpose, known techniques have to be extended and new methods have to be learnt additionally. The lecture introduces the theoretical limits very quickly and follows with a discussion on the basic concepts of channel coding, including block codes. Based on this, modern error correction methods like LDPC codes, spatially coupled codes, and Polar codes are treated in depth. The lecture ends with a view on the application of channel coding in classical and distributed storage scenarios and in computer networks. Many of the applications are illustrated with example implementations in software (python/MATLAB).
The modul grade is the grade of the oral exam.
- Lecture attendance time: 15 * 3 h = 45 h
- Presence time Exercise: 15 * 1 h = 15 h
- Lecture preparation / revision: 15 * 3 h = 45 h
- Exercise: 15 * 1 h = 15 h
- Exam preparation and attendance: 60 h
Total workload: approx. 180 h = 6 LP
Previous attendance of the lectures "Communication Engineering I" and "Probability Theory" is recommended. Knowledge from the lecture "Applied Information Theory" can be helpful. Previous attendance of the lecture “Verfahren der Kanalcodierung” can be helpful, but is not necessary.
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
Minor Studies: Mathematics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MATH-105916 | Combinatorics | Irreg. | 9 | Aksenovich |
The final grade is given based on the written final exam (2h).
By successfully working on the problem sets, a bonus can be obtained. To obtain the bonus, one has to achieve 50% of the points on the solutions of the exercise sheets 1-6 and also of the exercise sheets 7-12. If the grade in the final written exam is between 4,0 and 1,3, then the bonus improves the grade by one step (0,3 or 0,4).
none
The students understand, describe, and use fundamental notions and techniques in combinatorics. They can analyze, structure, and formally describe typical combinatorial questions. The students can use the results and methods such as inclusion-exclusion, generating functions, Young tableaux, as well as the developed proof ideas, in solving combinatorial problems. In particular, they can analyze the existence and the number of ordered and unordered arrangements of a given size. The students understand and critically use the combinatorial methods. Moreover, the students can communicate using English technical terminology.
The course is an introduction into combinatorics. Starting with counting problems and bijections, classical methods such as inclusion-exclusion principle and generating functions are discussed. Further topics include Catalan families, permutations, Young tableaux, partial orders, and combinatorial designs.
The grade of the module ist the grade of the written exam.
Total workload: 240 hours
Attendance time: 90 hours
Self-study: 150 hours
Knowledge of the modules Linear Algebra 1 and 2 and Analysis 1 and 2 is recommended.
Lecture + Tutorial (4+2 weekly contact hours)
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113925 | Compiler Design | Irreg. | 9 | Platzer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- Distinguish the main phases of a state-of-the-art compiler
- Understand static and dynamic semantics of an imperative language
- Develop parsers and lexers e.g. with parser generators, combinators
- Perform semantic analysis
- Translate abstract syntax trees to intermediate representations and static single assignment form
- Analyze the dataflow in an imperative language
- Perform standard compiler optimizations
- Generate assembly code
- Allocate registers using a graph-coloring algorithm
- Understand opportunities and limitations of compiler optimizations
- Appreciate design tradeoffs how representation affects optimizations
- Automatically manage memory using garbage collection
- Develop complex software following high-level specifications
This course covers the design and implementation of compiler and runtime systems for high-level programming languages, and examines the interaction between language design, compiler design, and runtime organization. Topics covered include lexical and syntactic analysis, semantic analysis, type-checking, program analysis, code generation and optimization, memory management, and runtime organization.
Compilers and principles of compiling are one fundamental core aspect of computer science. Compilers and several other parts of compiler technology (especially parsing, transformation, analysis, and optimization) play important roles in many systems built every day. The knowledge gained in this course should be broad enough that if you are confronted with the task of contributing to the implementation of a real compiler in the field or similar technology, you should be able to do so confidently and quickly.
9 ECTS from 270h of coursework consisting of
- 60h=15*4h from 4SWS lectures
- 90h preparation, reading lecture notes, studying
- 100h developing a compiler
- 20h exam preparation
Students are expected to have significant experience in a high-level programming language. Students are also expected to follow the lecture notes.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114251 | Computational Geometry | Irreg. | 6 | Bläsius |
| T-INFO-114252 | Computational Geometry - Pass | Irreg. | 0 | Bläsius |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students develop a systematic understanding of questions and solution approaches in the field of computational geometry, building on their existing knowledge of theoretical computer science and algorithms. Upon successful completion of the course, students will be able to:
* explain concepts, structures, and fundamental problem definitions presented in the lectures
* execute geometric algorithms, analyze them mathematically, and prove their properties
* select appropriate algorithms and data structures for solving a given geometric problem and adapt them to specific problem scenarios if necessary
* analyze unfamiliar geometric problems, reduce them to their algorithmic core, and create an abstract model; based on the concepts and techniques learned in the lecture, design their own solutions within this model, analyze them, and prove their properties
Spatial data is processed in a wide variety of areas in computer science, such as computer graphics and visualization, geographic information systems, robotics, and more. Computational geometry focuses on the design and analysis of geometric algorithms and data structures. This module introduces frequently used techniques and concepts in computational geometry, which are explored in depth using selected and application-related questions.
Lecture with exercises, 4 hours per week (SWS), 6 ECTS corresponds to approximately 180 hours of work, including: ~60 hours attending lectures and exercises ~30 hours preparation and review ~60 hours working on exercise sheets ~30 hours exam preparation
Basic knowledge of algorithms and data structures (e.g., from the courses Algorithms 1 + 2) is expected.
| Coordinators: |
Prof. Dr.-Ing. Jürgen Beyerer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112573 | Computational Imaging | WT | 5 | Meyer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification goal: Students are able to model questions of machine vision optically and algorithmically and to process them using holistic optimization.
Learning objectives: Students know
- the essential components of machine vision, their optical modelling and suitable coding methods in the sense of computational imaging,
- methods for emitting, capturing and processing light fields for applications in photography and industrial image processing,
- the concept of light transport analysis, corresponding modelling, capturing and processing methods and
- approaches to holistic modelling and optimization of optical image capturing and processing systems.
Digital image acquisition and processing have revolutionized various fields of applications, e.g., medical imaging or automated visual inspection. Yet, the design of most such systems is still based on the separate and individual optimization of the employed illumination, image acquisition and image processing components. By following a holistic approach for system design, modelling and optimization, computational imaging methods yield superior performance with respect to the state of the art. After introducing the students into relevant basics of optics and signal theory, the lecture will thoroughly cover various topics of computational imaging. Accompanying practical exercises will complement the theoretical part of the lecture. The course will enable students to adequately model artificial vision problems in the sense of computational imaging in order to obtain holistically optimal solutions.
Lecture with 2 SWS + 1 SWS exercise
5 ECTS corresponds to approx. 150 hours
approx. 30 hours lecture attendance,
approx. 15 hours exercise attendance,
approx. 90 hours post-processing and working on the exercises
approx. 30 hours Exam preparation
- Ayush Bhandari, Achuta Kadambi, Ramesh Raskar, Computational Imaging, MIT Press, 2022.
- Jürgen Beyerer, Fernando Puente León, Christian Frese, Machine Vision, Springer, 2015.
- Joseph. W. Goodman, Introduction to Fourier Optics. 4. Auflage W. H. Freeman, 2017.
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112704 | Constructive Logic | ST | 5 | Platzer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- Understand the working principles of logic
- Understand how the meaning of a proposition comes from its verifications
- Distinguish propositions from judgments
- Use proof rules to conduct formal proofs
- Formalize informal problems into precise logical language
- Justify how proof rules fit to one another in sound and complete ways
- Assess the validity of a formal proof
- Understand propositions as types, proofs as programs, formulas as
programs
- Relate constructive logic to computation and constructive proofs to
functional programs
- Relate deductive proof search to computation in logic programming
- Relate induction to recursion and use induction to prove properties in and
about logical systems
- Understand the principles and applications of logic programming
This course provides a thorough introduction to modern constructive logic, its roots in philosophy, its numerous applications in computer science, and its mathematical properties. The core topics of this course are intuitionistic logic, natural deduction, Curry-Howard isomorphism, propositions as types, proofs as programs, formulas as programs, functional programming, logic programming, Heyting arithmetic and primitive recursion, cut elimination, connections between classical and constructive logic, inductive definitions, sequent calculus, and decidable classes. Advanced topics may include type theory, proof search, linear logic, temporal logic, modal logic.
Course web page: https://lfcps.org/course/constlog.html
5 ECTS from 150h of coursework consisting of
45h = 15 * 3h from 3 SWS lectures
15h = 15 * 1h from 1 SWS exercises
90h preparation, reading lecture notes, studying
22h exam preparation
You will be expected to follow the lecture notes.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114602 | Cryptographic Protocols II: Universal Composability | WT | 3 | Müller-Quade |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student
- understands the problems that occur in protocol composition and can explain them using examples.
- knows and understands the frameworks, security concepts and their properties presented in the lecture and can explain, relate and prove
these.
- knows and understands the protocols and proofs presented in the lecture and can reproduce them.
- can independently apply the techniques presented in the lecture, for example to prove or demonstrate the security or insecurity of simple protocols.
In the lecture "Kryptographische Protokolle", methods and building blocks for secure multi-party computation were presented. The security was shown for a single execution and under sequential composition.
In reality, however, protocols are executed concurrently - both multiple instances of the same protocol (concurrent composition) and multiple, different protocols (general composition) that were designed independently of each other.
This protocol composition is not adequately covered by classical security concepts: it may be that the execution of a single protocol instance can be proven to be secure. However, if the same protocol is executed several times simultaneously, all security may be lost.
In this lecture, we re-visit the topics of the “Kryptographische Protokolle” lecture with a focus on composability. The first part of the lecture therefore introduces the concept of universally composable (UC) security, which depicts the setting of general composition in which arbitrary protocols are executed concurrently. An important insight here is that UC security can only be achieved with the help of trust assumptions, so-called "setups". In the second part of the lecture, important protocols that fulfill this strong security are presented, for example for commitments or general secure multi-party computation. At the end of the lecture, further composing security notions and related protocols are considered, which are weaker than UC security, but without setups.
90 h
Lectures: 24 h
Self-study during semester: 31 h
Preparation for the exam: 35 h
Students should be familiar with the contents of the modules "Theoretische Grundlagen der Kryptographie" (Theoretical Foundations of Cryptography) and „Kryptographische Protokolle“ (Cryptographic Protocols I).
| Coordinators: |
TT-Prof. Dr. Benjamin Schäfer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Area of Specialization: Human-centred Machine Intelligence Area of Specialization: Data Science Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113402 | Data Science and Artificial Intelligence for Energy Systems | ST | 6 | Schäfer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
• Students obtained a foundational knowledge of data-driven methods in energy systems as an active research field. They can name some ongoing challenges.
• They can explain different data science methods and their applications in energy systems (including Langevin processes, superstatistics, (probabilistic) forecasts and explainable AI).
• Students can employ AI methods to solve problems in energy systems, including optimizing systems and forecasting time series.
• Students can exploit key properties of trained machine learning models and interpretability tools.
• Students can select suitable analysis tools, justify their choice and carry out data-driven analysis on power systems .
Artificial Intelligence (AI) is a key technology in many areas of society and research. Energy systems with the ongoing energy transition (“Energiewende”) make it a fascinating field for deploying AI methods. AI and machine learning algorithms can play a crucial role in improving energy efficiency, optimizing power generation and distribution or enhancing system stability while facilitating additional renewable energy integration. In this lecture, we review some mechanics of energy systems, their design and optimization questions and how to solve these using data-driven approaches. We will discuss deterministic dynamics, as well as stochastic aspects of energy systems and will explore fundamental AI algorithms and their applications in energy systems. We will cover both classical time series methods as well as state-of-the-art AI techniques, e.g. for optimization or forecasting.
Course workload:
1. Attendance time: 4 SWS x 15=60 (Course, exercise, etc.)
2. Self-study: 6 h x 15 = 90 (independent review of course material,
work on homework assignments)
3. Preparation for the exam: 30h
60+90+30=180h= 6ECTS
Knowledge of AI basics is very helpful.
Previous participation in “Energieinformatik 1” and/or “Energieinformatik 2” is beneficiary but not mandatory.
Knowledge of Python is highly recommended.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110820 | Decentralized Systems: Fundamentals, Modeling, and Applications | ST | 6 | Hartenstein |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Fundamentals & Modeling
1. The student is able to recognize and distinguish distributed, federated, and decentralized systems.
2. The student understands the role of consensus, consistency, and coordination within the context of networked and decentralized systems.
3. The student understands the concept of Sybil attacks.
4. The student is familiar with decentralized algorithms like leader election for execution contexts with various guarantees.
5. The student understands the formally proven limits of fault tolerance and their underlying assumptions. This includes an understanding of synchronous and asynchronous network models which underpin the respective proofs. The student also understands several models for fault tolerance within the context of decentralized and distributed systems.
6. The student has a basic understanding of state machine replication.
7. The student knows how conflict-free replicated data types employ mathematics to provide eventually consistent data to collaborative peer-to-peer applications.
Applications
1. The student understands how decentralized storage, replication, and coordination is employed in practice.
2. The student has a fundamental understanding of blockchain-based systems and payment channels, as well as decentralized systems for storage, communication, and machine learning.
3. The student understands trust relations in distributed and decentralized systems applications.
4. The student is able to understand how theoretical foundations relate to networked and decentralized systems in practice.
Decentralized Systems (like blockchain-based systems) are distributed systems under the control of multiple, independent parties that act on their own, autonomous decisions. In this course, we cover fundamental theoretical aspects as well as up-to-date decentralized systems, and connect theory with current practice. We thereby address fault tolerance, security and trust, as well as performance aspects at the example of applications like Bitcoin, Ethereum, IPFS, Matrix, or the Fediverse. As a research-oriented lecture, we cover additional current topics like decentralizing artificial intelligence via decentralized, federated learning, or identity and access management in decentralized systems.
The lecture covers at least the following topics:
1. Attendance time (Course, exercise, etc.)
Lecture: 3 SWS: 3,0h x 15 = 45h
Exercise: 1 SWS: 1,0h x 15 = 15h
2. Self-study (e.g. independent review of course material, work on homework assignments)
Weekly preparation and follow-up of the lecture: 15 x 1h x 3 = 45h
Weekly preparation and follow-up of the exercise: 15 x 2h = 30h
3. Preparation for the exam: 45 h
Σ = 180h = 6 ECTS
Basics according to the lectures "Information Security" and "Introduction to Computer Networks" are recommended.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114219 | Deep Learning and Neural Networks | ST | 6 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students will learn about the structure and function of different types of neural networks.
Students should learn the methods for training the various networks and their application to problems.
Students should learn the areas of application of the different types of networks.
Given a concrete scenario, students should be able to select the appropriate type of neural network.
This module introduces the use of neural networks for the solution of solving various problems in the field of machine learning, such as classification, prediction, control or inference. or inference. Different types of neural networks are covered and their areas of application are illustrated using examples.
Lecture with 4 SWS, 6 CP.
6 LP corresponds to approx. 180 hours, of which
approx. 60 hours lecture attendance
approx. 90 hours post-processing (self-study)
approx. 30 hours exam preparation (self-study)
Prior successful completion of the core module "Cognitive Systems" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Rainer Stiefelhagen
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
(Usage from 10/1/2026)
Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115257 | Deep Learning for Computer Vision: Advanced Topics | WT | 3 | Stiefelhagen |
See partial achievements.
See partial achievements.
Students gain an understanding of advanced deep learning methods and tasks, especially with regard to their applications in image processing (computer vision). Students are able to apply deep learning methods for special tasks, such as learning with little training data, changes in the data domain or uncertainty determination.
Deep convolutional neural networks (CNNs) achieve excellent results in many areas of computer vision, but face challenges in real-world applications, such as dependence on costly annotated training data, high computing power or difficult traceability of decision paths. While for a long time the development of recognition algorithms was primarily driven by high recognition rates on large and cleanly annotated data sets, today application-relevant goals such as learning with little training data, explainability, uncertainty estimation or domain adaptation are becoming increasingly important.
The lecture deals with advanced network architectures, learning methods and research areas in the field of deep learning for computer vision. Among others, the following topics will be covered:
See partial achievements.
Attendance of the lectures: approx. 20 hours
Preparation and follow-up of the lecture: approx. 30 hours
Exam preparation: approx. 40 hours
Total: approx. 90 hours
Knowledge of deep learning basics is strongly recommended.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114254 | Design and Architectures of Embedded Systems (ESII) | WT | 3 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student learns methods for mastering complexity and applies these methods to the design of embedded systems. He/she evaluates and selects specific architectures for embedded systems. Furthermore, the student receives an introduction to current research topics.
Nowadays, it is possible to integrate several billion transistors on a single chip and thus realize complete SoCs (systems-on-chip). The trend towards being able to use more and more transistors continues unabated, meaning that the complexity of such systems will also continue to increase. Computers will increasingly be ubiquitous, i.e. they will be integrated into the environment and will no longer be perceived as computers by humans. Examples include sensor networks, electronic textiles and many more. However, the physically possible complexity will not be readily achievable in practice, as there is currently a lack of powerful design processes capable of handling this high level of complexity. Powerful ESL tools ("Electronic System Level Design Tools") and novel architectures will be required. The focus of this lecture is therefore on high-level design methods and architectures for embedded systems. Since the power consumption of (mostly mobile) embedded systems is of crucial importance, one focus of the design methods will be on the design with regard to low power consumption.
90 hours:
30 hours of lectures (15 weeks*2 hours)
30 hours of self study (15 weeks*2 hours)
15 hours of deepening (coding low level C and embedded C) to grasp the theory with practical
15 hours exam preparation
| Coordinators: |
Prof. Dr. Ann-Kristin Kupfer
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
Minor Studies: Economics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-WIWI-112693 | Digital Marketing | ST | 4,5 | Kupfer |
| Supplementary Courses (Elective: 4,5 credits) | ||||
| T-WIWI-114174 | Economic Decision Making | WT | 4,5 | Scheibehenne |
| T-WIWI-114893 | International Entrepreneurship and Brand Development | see notes | 6 | Kupfer, Terzidis |
| T-WIWI-115326 | AI- and Data-Driven B2B Sales | ST | 1,5 | Klarmann, Konhäuser |
| T-WIWI-107720 | Market Research | ST | 4,5 | Klarmann |
| T-WIWI-112711 | Media Management | see notes | 4,5 | Kupfer |
| T-WIWI-111848 | Online Concepts for Karlsruhe City Retailers | ST | 3 | Kupfer |
The assessment is carried out as partial exams of the core course and further single courses of this module, whose sum of credits must meet the minimum requirement of credits of this module. The assessment procedures are described for each course of the module separately.
The overall grade of the module is the average of the grades for each course, weighted by the credits and truncated after the first decimal.
None
Students
The "Digital Marketing" module explores advanced marketing strategies within the context of digital transformation. Students develop a deep understanding of digital consumer behavior, the digital environment, and digital marketing strategies:
By integrating these multidisciplinary perspectives, the module enables students to implement sophisticated marketing instruments and critically evaluate current practices in digital business environments.
Total effort for 9 credit points: approx. 270 hours.
The exact distribution is done according to the credit points of the courses of the module.
| Coordinators: |
Prof. Dr. Achim Streit
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114235 | Distributed Computing | WT | 4 | Streit |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
Students understand the basic concepts of distributed systems, in particular Grid and Cloud Computing as well as the management and analysis of big and distributed data. They apply underlying paradigms and services to given examples. Students analyze methods and technologies of Grid and Cloud Computing as well as distributed data management, which are suitable for use in everyday and industrial application areas or which are used today by Google, Facebook, Amazon, etc. For this purpose, students will compare web/grid services, elementary grid functionalities, data lifecycles, metadata, archiving, cloud service types (IaaS, SaaS, PaaS) and public/private clouds
using real-world examples.
The lecture introduces the world of distributed computing with a focus on fundamentals and technologies from Grid and Cloud Computing as well as the handling of Big Data. The lecture combines theory and
application with the help of relevant examples from science and industry.
First, an introduction to the main characteristics of distributed systems is given. Then the topic of Grid Computing is discussed in more detail and the close relationship between Grid computing and distributed data management is illustrated using the example of the WLCG, the infrastructure for distributing, storing and analyzing data from the particle accelerator at CERN.
Subsequently, the topic of cloud computing is discussed and compared with the preceding. After the definition of basic terms and concepts, virtualization is introduced as one of the key technologies of Cloud Computing; finally, common architectures, services and components in the Cloud context are discussed using examples and in general.
Next, common methods for authorization and authentication in distributed environments will be discussed. The lecture includes the description of the basics of Authentication and Authorization Infrastructures (AAI) as well as different technologies, for example certificate- or token-based procedures.
In a further block of topics, concepts for the management and analysis of large or distributed data are presented. In this context tools and frameworks, as well as the lifecycle of data, its metadata and data storage are explained.
2 SWS = 120 h per semester
• 30 h in the weekly lecture during the semester
• 90 h post-processing of lectures and self-learning of the content due to its complexity
| Coordinators: |
Dr. Victor Pankratius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114258 | Edge-AI in Software and Sensor Applications | WT+ST | 3 | Pankratius |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification goals
After completing the module, students have the following skills. They...
- can name and explain the theoretical and practical aspects of software and sensor technology in the context of edge and fog computing
- can name and use techniques of software engineering and algorithm development for sensor applications
- can name and use methods of artificial intelligence in the context of resource constraints and fault tolerance
- can weigh the characteristic properties of the methods and tools presented, their advantages and disadvantages against each other and can select a suitable tool for a given application scenario.
Learning objectives
Students can name the relevant elements of a technical system and their tasks in edge/fog computing. Students are able to name resource constraints of different types (CPU, memory, communication, energy) and describe their effects on software and algorithm design. Students can describe functional principles of sensors of different types (e.g. microelectromechanical systems - MEMS), describe their functional principles in accelerators, gyroscopes, pressure/humidity sensors, particle detection, etc., explain applications and their context (e.g. gesture recognition in mobile phones/"wearables"/"hearables", localization & navigation, environmental measurements). Students are able to design software systems for edge and fog applications and to develop complex edge and fog software projects in an engineering manner. The problems and requirements of different application areas can be recognized, processed and transferred to a new context. Problems in recognizing patterns in sensor data, classification and prediction can be solved using model-based algorithms or machine learning approaches. Problems in deriving instructions for action can be solved using inference techniques.
Edge computing comprises applications, data and services that are relocated to the outer edges of networks. Such systems typically require local data processing with limited resources such as energy consumption, CPUs, memory or connectivity. Fog computing also combines these aspects with cloud architectures. The importance of these approaches is growing today for modern sensor applications and ranges from industrial applications to Internet-of-Things, ubiquitous computing, consumer applications in cell phones, wearables & hearables (e.g. health & fitness applications), drones or applications in augmented reality. At the same time, the proportion of hardware-related software is also growing in all sensor applications, which opens up new possibilities. In this context, artificial intelligence methods are becoming increasingly important in order to realize learning systems with improved autonomy and immediate feedback. This module presents the current status as well as research work and open problems.
2 SWS: (2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h = 3 ECTS
Knowledge of e.g. cognitive systems, software engineering, algorithms, computer networks & structures, low-power design is helpful.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111549 | Embedded Machine Learning Lab | WT+ST | 4 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student will understand the main concept of machine learning (ML) on embedded systems, the constraints present on such platforms, and the design objectives for ML algorithms on such platforms. The student will be able to understand various concepts of compression of neural networks. The student will gain hands-on experience with current state-of-the-art ML frameworks, parameter tuning of algorithms, and will develop software programs for implementing the concepts. The student will be able to compare and analyze the current state-of-the-art algorithms regarding their flexibility and performance on embedded devices.
IoT devices more and more rely on ML models to perform their operations. They thereby also generate lots of data that should be used to improve these ML models through on-device learning. Devices need to perform the training with this data locally due to privacy constraints or communication limitations. However, the inference of neural networks, and especially the training, requires too many resources (computations, memory, energy, etc.) — unless the available resources are considered in the design.
This lab provides insights into deploying machine learning algorithms to embedded devices.
Since embedded devices operate with significantly lower resources than the commonly-employed high-end GPUs, making neural networks run fast without sacrificing much accuracy on embedded devices is a challenging task. The lab covers training and inference on resource-constrained devices, introducing state-of-the-art methodologies like pruning and quantization.
The students will learn about neural networks beyond theory, working with popular frameworks like TensorFlow, the effects of hyperparameters, and how they influence the network. Furthermore, the student will learn about resource and accuracy trade-offs in neural networks and design custom networks to achieve given resource or accuracy requirements.
This lab requires basic (theoretic) knowledge about neural networks and training. Further knowledge of Linux environments and Python is strongly advised since they will be intensively used in the lab and are the de-facto industry standard for machine learning research.
The students will meet every week. Exact dates and times will be fixed in the first kick-off meeting. Depending on the number of participants, students will work together in groups of 2-3 students.
(2 SWS +1.5*2 SWS)*10
+55 h final project
+15 h presentation & report
= 120 h = 4 ECTS
This lab requires a basic (theoretic) knowledge about neural networks and training. Further knowledge of Linux environments and Python is strongly advised since they will be intensively used in the lab and are the de-facto industry standard for machine learning research.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115054 | Empirical Software Engineering | WT+ST | 4 | Dehghani |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
The lecture deals with the role of empiricism in software engineering. It introduces the most common empirical methods and points out common sources of error in empirical studies. The associated statistical methods for analyzing and presenting the data are taught. The lecture uses a number of scientific publications to illustrate the concepts and bring them to life.
The lecture will take place once (one 1.5h session) per week. The workload will amount to 4.0 CP, as students will also complete exercises in preparation for the exam.
| Coordinators: |
Prof. Dr. Raffaela Mirandola
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113349 | Engineering Self-Adaptive Systems | WT | 3 | Mirandola |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- Understand the motivation for self-adaptation
- Get familiar with the basic principles and conceptual model of self-adaptation
- Understand how to engineer self-adaptive software systems from a software engineering perspective
- Understand the decision-making process using formal analysis at runtime for quality assurance
- Understand the notion of uncertainty in self-adaptive systems and how to tame it with formal verification at runtime
- Understand the level of adoption of self-adaptive systems in industry.
Self-adaptation is an important field of research and engineering that aims to address the challenging problem of how to engineer software systems that have to deal with uncertainties that can only be resolved at run time.
The course presents the basic principles of self-adaptation and introduces a conceptual feedback loop model of a self-adaptive system. It introduces quality models which can be used to estimate quality properties at runtime by a self-adaptive system to provide guarantees for the quality goals. The role played by the different types of uncertainties is then explored analyzing different possible approaches.
Course workload:
30h in Class (lectures)
45h self-study during the semester
15h preparation for the exam
| Coordinators: |
Gustavo Gil Gasiola
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Minor Studies: Law
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113887 | EU Data Protection Law | WT | 3 | Gil Gasiola |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to comprehend the EU data protection regulation, including the General Data Protection Regulation and related EU data regulations.
They know the foundations of data protection rules, including fundamental concepts (e.g., “personal data”, “processing”, “data subject”). They are also familiar with the principles of personal data processing (lawfulness, limited purpose, transparency, accountability) as well as the rights of the data subject.
They can identify the main obligations of the controller and the processor.
Students understand the conditions for the transfer of personal data to third countries.
They can identify the other regulations that govern data in the European Union.
Students are able to read and understand legal text related to data regulation.
They can understand and solve simple data protection cases.
The General Data Protection Regulation (GDPR) of the European Union is a milestone in protecting individuals from the unlawful use of their data. In a data-driven society, economy, and government, this protection has become essential to guarantee fundamental rights. In addition to its direct impact on the legal systems of all Member States, the GDPR has a major influence on third countries that have adopted similar regulations (e.g. Switzerland, Argentina, Brazil, South Africa, and many others). In this way, the EU Data Protection Regulation has established itself as the “gold standard” of data protection, providing guidance to address the challenges posed by new technologies and new ways of creating, using and sharing personal data. Understanding the structure of data protection in the EU is therefore essential to grasp its impact on individual rights, public administration, business models, and even technological development.
This lecture aims to provide a structured overview of the EU Data Protection Regulation, and to offer tools to understand the regulatory structure of the EU Data Regulation. The lecture will cover the following topics:
- Introduction to EU law
- Development of the EU data protection regulation
- Legal structure of data protection in the EU
- Role of national and sectoral laws
- Data protection as fundamental right
- Principles of data protection
- Lawfulness of personal data processing
- Anonymization and pseudonymization of personal data
- Special categories of personal data
- Rights of the data subject
- Transfer of personal data to third countries
- Responsibility of the controller and the processor
- Security of personal data and personal data breach
- Open Data Directive
- Data Governance Act
- Data Act
- Attendance time to the lectures = 15 x 90 min = 22 h 30 min
- Self-study during the semester = 47 h 30 min
- Preparation for the exam = 20 h
- Total = 90 h
| Coordinators: |
TT-Prof. Dr. Frederike Zufall
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Minor Studies: Law
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115479 | EU-Law | WT | 3 | Zufall |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students will gain a general understanding of the European Union, EU law and its interplay with the law of the Member States. Moreover, they will be able to distinguish between the different actors and sources of law. Students will also become familiar with the European single market and the fundamental freedoms of the European Union as well as European human rights. After the lecture, students should be able to identify and solve issues of EU law.
The lecture is designed to provide students with a general understanding of the European Union, EU law and its interplay with the law of the Member States. The lecture will introduce students to the institutional structure of the European Union, including the main actors (European Commission, Council of the European Union, European Parliament and Court of Justice of the European Union). Moreover, an overview over the legislative process of the European Union and the different sources of law (in particular regulations, directives and decisions) will be provided.
Another focus of the lecture will be the European single market and the fundamental freedoms. The lecture will also discuss European human rights (in particular the Charter of Fundamental Rights).
Furthermore, the lecture will deal with the relationship between EU law and the law of the Member States as well as the application of EU law by national authorities and the relationship between national courts and the courts of the European Union.
The course workload is 90 h, including 22,5 h attendance time.
Students are advised to have the EU Treaties and other relevant legal texts at hand (e.g. Blackstone’s EU Treaties & Legislation or online at https://eur-lex.europa.eu/collection/eu-law/treaties/treaties-force.html ).
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Area of Specialization: Data Science (Usage from 10/1/2025) Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112774 | Explainable Artificial Intelligence | ST | 3 | Lioutikov |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
• Students are able to understand problems and challenges of XAI
• Students can identify and differentiate different types and approaches of XAI
• Students can implement various XAI approaches
• Students understand current research questions and directions of XAI
Recent advances in Machine Learning and Deep Learning in particular have lead to the imminent introduction of AI agents into a wide variety of applications. However, the apparent “black-box” nature of these approaches hinders their application in both critical systems and close human-robot interactions. The sub-field of eXplainable Artificial Intelligence (XAI) aims to address this shortcoming. This lecture will introduce and discuss various concepts and methods of XAI and consider them from perspective of Robot Learning and Human-Robot Interaction.
The lecture will start with a (brief) introduction into relevant deep learning approaches, before discussing interpretable scene, task and behavior representations. Afterward the lecture will consider itself with Data-Driven and Goal-Driven AI. Finally, first approaches that incorporate XAI and XAI-based human feedback directly into the learning process itself will be discussed. An exemplary list of topics is given below:
• Introduction to XAI
◦ Interpretable Machine Learning vs Explainable Machine Learning
• Primer / Introduction to relevant Deep Learning Concepts
◦ MLPs and CNNs
◦ Graph Neural Networks
◦ Transformers
◦ Diffusion Models
◦ Score Based Methods
• Interpretable Structures
◦ Scene Representations
◦ Task Representations
◦ Behavior Representations
• Data-Driven Explainable AI: XAI Methods for
◦ Shapley Values
◦ Saliency Maps
◦ Concept Activation Vectors
◦ Linguistic Neuron Annotation
• Goal-Driven Explainable AI: XAI Methods for
◦ Generative Explaining Models
◦ Behavior Verbalization
◦ Behavior Visualization
• Interactive Learning
◦ Integrating Human Feedback
◦ Explanatory Interactive Learning
Workload = 90h = 3 ECTS
- ca 30h lecture attendance
- ca 30h post-processing
- ca 30h exam preparation
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python / PyTorch experience could be beneficial when we discuss practical examples/implementations.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113391 | Fine-Grained Complexity Theory & Algorithms | Irreg. | 6 | Künnemann-Goos |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know the foundations of fundamental algorithmic barriers in the polynomial-time and exponential-time regimes.
They are able to use fine-grained reductions to relate the time complexity of different problems. They can derive conditional lower bounds from such reductions, based on established hardness assumptions.
Furthermore, they know about the techniques underlying the fastest known algorithms for central problems in the field.
- fine-grained reductions:
-- conditional lower bounds
-- main techniques for obtaining such reductions
- central hardness assumptions and their applications:
-- (Strong) Exponential Time Hypothesis
-- Orthogonal Vectors Hypothesis
-- 3SUM Hypothesis
-- APSP Hypothesis
- conditional lower bounds for string problems, algorithmic graph theory, geometry
- algorithmic techniques:
-- fastest known algorithms for central problems (SAT, Orthogonal Vectors, 3SUM, APSP)
-- polynomial method
-- applications of fast matrix multiplication
-- Fast Fourier Transform/polynomial multiplication
Lecture with exercises, 4 SWS, 6 CP
6 CP amounts to 180 h, distributed as follows:
- about 60 h attendance of lectures and exercise sessions
- about 30 h of preparation and reviewing course material
- about 60 h solving exercise sheets
- about 30 h exam preparation
Basic knowledge of theoretical computer science and algorithm design is recommended.
| Coordinators: |
Jun.-Prof. Dr. Jan Stühmer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112662 | Geometric Deep Learning | WT | 6 | Stühmer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students gain a theoretical and methodical approach to modern Deep Learning as well as knowledge and experience about the application of Deep Learning methods on networks and graphs
Students are able to apply this knowledge for understanding existing state-of-the-art Deep Learning architectures and for deriving novel architectures from first principles
- This module provides students with both theoretical and practical insights into modern Deep Learning
- In particular, we focus on a novel approach for understanding deep neural networks with mathematical tools from geometry and group theory
- This enables a methodical approach to Deep Learning: starting from first principles of symmetry and invariance, we derive different network architectures for analyzing unstructured sets, grids, graphs, and manifolds
- Topics of the course include: group theory, graph neural networks, convolutional neural networks, applications of geometric deep learning in diverse fields such as geometry processing, molecular dynamics, social networks, game playing (computer Go), processing of text and speech, as well as applications in medicine
Lecture with 2 SWS + 2 SWS Tutorial, 6 LP
6 LP corresponds to 180 hours, including
1) Attendance of the course: 22.5h (15x90min slots)
2) Attendance of the exercise sessions: 22.5h (15x90min slots)
3) Self-study of course material and work on homework assignments: 60h (4h/week)
4) Preparation for the exam: 75h (self-study)
Knowledge about the foundations of machine learning, group theory and linear algebra useful but not required.
| Coordinators: |
Prof. Dr. Peter Sanders
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114232 | Graph Partitioning and Graph Clustering in Theory and Practice | Irreg. | 4 | Sanders, Ueckerdt |
| T-INFO-114233 | Graph Partitioning and Graph Clustering in Theory and Practice - Practical | Irreg. | 1 | Sanders, Ueckerdt |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The aim of the lecture is to provide students with an initial insight into the problems of graph partitioning and graph clustering and to apply knowledge from graph theory and algorithmics.
On the one hand, the problems that arise are reduced to their algorithmic core and then solved efficiently. On the other hand, various modelling methods and their interpretations are discussed. After successfully completing the course, students will be able to apply the methods and techniques presented autonomously to related problems.
Many applications in computer science involve the clustering and partitioning of graphs, e.g. the finite element method in scientific simulations, digital circuit design, route planning, web graph analysis or the analysis of social networks.
A well-known example where good partitioning of unstructured graphs is needed is parallel processing, where graphs must be partitioned to distribute computations evenly over a given number of processors and minimise communication between them. k processors, the graph must be divided into k blocks of approximately equal size so that the number of edges between the blocks is minimal.
Since many partitioning and clustering problems occur in practice, the problems discussed are introduced and motivated, and both the theoretical and practical aspects of graph partitioning and graph clustering are taught, including heuristics, meta-heuristics, evolutionary and genetic algorithms as well as approximation and streaming algorithms.
Lecture with project/experiment with 3 SWS, 5 CP correspond to approx. 150 working hours, of which
approx. 30 hours attending the lecture
approx. 60 hours of preparation and follow-up work
approx. 30 hours working on the project/experiment
approx. 30 hours exam preparation
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
Minor Studies: Mathematics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MATH-102273 | Graph Theory | Irreg. | 9 | Aksenovich |
The final grade is given based on the written final exam (3h).
By successfully working on the problem sets, a bonus can be obtained. To obtain the bonus, one has to achieve 50% of the points on the solutions of the exercise sheets 1-6 and also of the exercise sheets 7-12. If the grade in the final written exam is between 4,0 and 1,3, then the bonus improves the grade by one step (0,3 or 0,4).
None.
The students understand, describe and use fundamental notions and techniques in graph theory. They can represent the appropriate mathematical questions in terms of graphs and use the results such as Menger’s theorem, Kuratowski’s theorem, Turan’s theorem, as well as the developed proof ideas, to solve these problems. The students can analyze graphs in terms of their characteristics such as connectivity, planarity, and chromatic number. They are well positioned to understand graph theoretic methods and use them critically. Moreover, the students can communicate using English technical terminology.
The course Graph Theory treats the fundamental properties of graphs, starting with basic ones introduced by Euler and including the modern results obtained in the last decade. The following topics are covered: structure of trees, paths, cycles and walks in graphs, minors, unavoidable subgraphs in dense graphs, planar graphs, graph coloring, Ramsey theory, and regularity in graphs.
Total workload: 240 hours
Attendance time: 90 hours
Self-study: 150 hours
Lecture + Tutorial (4+2 weekly contact hours)
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-103009 | Hands-on Bioinformatics Practical | Irreg. | 3 | Stamatakis |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The participants develop and document an open-source tool or pipeline for sequence-based data analysis of biological data. The tool is likely to cover one or more of the main topics of the corresponding lecture and shall be useful to and usable for the biological user community. If possible, the tool should be published in a peer-reviewed scientific journal. Participants learn to work in teams of 2-3 programmers, to use version management tools such as github, to analyse and optimise the runtime behaviour of programs using appropriate tools, to test C/C++ programs for memory leaks (e.g., using valgrind), and to improve the quality of their code using SoftWipe (https://www.nature.com/articles/s41598-021-89495-8). Participants will be able to independently carry out and document larger software projects in the field of bioinformatics and evaluate as well as improve code quality. They are able to write a scientific paper in a team.
In the practical course, we jointly develop an open-source tool (algorithms, analysis pipelines, parallelisation) with the aim of providing a new tool that is useful for biology and can be used by biologists at the end of the semester.
Weekly meetings with the supervisor 15 hours + internal team meetings 15 hours + programming time 45 hours + 15 hours writing paper or final report = 90 hours = 3 ECTS
| Coordinators: |
Dr.-Ing. Jens Becker
Prof. Dr.-Ing. Jürgen Becker
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-100672 | Hardware Modeling and Simulation | WT | 4 | Becker, Becker |
Achievement is examined in the form of a written examination lasting 120 minutes.
none
After completing this module, students will be familiar with different hardware description languages and their applications in various abstraction levels. They will gain knowledge of the SPICE Hardware Description Language and become proficient in building and deriving the analog matrix for spice simulation. In the realm of digital design, they will develop a comprehensive understanding of the hardware description language VHDL, encompassing the VHDL Standard and its extensions, such as VHDL 2008, the 9-valued logic, and the VHDL-AMS standard. Furthermore, students will achieve a profound comprehension of simulator principles, particularly the delta cycle model. They will also grasp the fundamentals of fault simulations for testing fabricated circuits and learn to derive test vectors. Additionally, students will acquire an understanding of higher-level hardware construction languages like Chisel and SystemC.
In order to address the complexity of modern chips during development, it is essential to utilize modern hardware description languages. This course offers insights into the various levels of abstraction in these languages. It starts by covering the fundamentals of analog description using SPICE and then progresses through VHDL, VHDL-AMS, and Verilog. Additionally, the course introduces more abstract languages like Chisel and SystemC.
Topics covered in the course are:
The module grade results from the grade of the written examination.
The workload is covered by:
Sum: 120h = 4 LP
| Coordinators: |
Prof. Dr.-Ing. Jürgen Becker
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-113922 | Hardware Synthesis and Optimization | ST | 6 | Becker |
The examination takes place within the framework of an oral overall examination (approx. 30 minutes)
none
Students know the basic steps required for the automated design of optimized digital circuits. They are able to classify them in the Y-chart and assess their complexity.
They will be able to name and explain the most important approaches for these design steps and evaluate them with regard to optimality and computational effort. This includes the ability to use algorithms for these approaches, e.g. selected graph algorithms, metaheuristics such as simulated annealing. The students are also able to determine their respective runtime complexities.
In addition, they can solve given problems from the field of design automation by selecting a suitable approach based on certain optimization criteria and applying it to the respective problem.
The module focuses on teaching the formal and methodological foundations for the automated design of optimized electronic systems. The relevant scientific and methodological properties of the methods used are discussed and their implementation in industrial practice is also taught.
The following topics are covered:
The module grade is the grade of the oral exam.
The workload includes (4 SWS):
1. attendance in lectures and exercises: 60 h
2. preparation / follow-up: 60 h
3. preparation of and attendance in examination: 60 h
A total of 180 h = 6 CR
Basic knowledge in the field of digital circuits, e.g. as taught in the course “Digital Technology” (2311615) is helpful.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114628 | Hardware-efficient Artificial Intelligence | WT | 3 | Henkel, Rapp |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Upon successful completion of the course, participants will be able to:
1. Understand and analyze the challenges of efficiently deploying deep learning models on hardware platforms.
2. Apply optimization techniques to improve the hardware efficiency of deep learning models, specifically to reduce latency, energy consumption, and memory usage.
3. Explain and implement the following methods in practical scenarios:
- Knowledge Distillation
- Neural Architecture Search (NAS)
- Pruning
- Quantization
4. Evaluate and adapt the use of these techniques for both cloud-based and embedded systems.
5. Perform model optimizations while maintaining high task performance.
Deep learning models are ubiquitous in modern applications, from computer vision to natural language processing. However, deploying these models efficiently on hardware platforms remains a significant challenge, leading to increased latency, energy consumption, memory usage, and cost. As deep learning models become larger and more complex, the need for optimization techniques to improve their performance on hardware is more critical than ever. This course discusses the principles and techniques for optimizing deep learning models for hardware deployment, focusing on both cloud and embedded systems.
The course covers various optimization techniques to improve model efficiency, reduce latency, and minimize memory usage while maintaining a high task performance. Precisely, the covered topics include
- Knowledge distillation
- Neural architecture search (NAS)
- Pruning
- Quantization
A basic understanding of deep learning concepts and neural networks is recommended. Furthermore, familiarity with hardware architectures and embedded systems is beneficial but not mandatory.
lecture time: 24h
self study: 24h
exam preparation: 42h
total: 90h = 3 ECTS
A basic understanding of deep learning concepts and neural networks is expected; familiarity with hardware architectures and embedded systems is helpful but not required.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113396 | HRI and Social Robotics | ST | 4 | Bruno |
| T-INFO-113397 | HRI and Social Robotics - Pass | ST | 2 | Bruno |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know the foundations of Human-Robot Interaction (HRI) and Social Robotics, including: design principles and methodologies, human factors influencing HRI (anthropomorphization), sensors, actuators and software architecture for social robotics, challenges and solutions for key abilities of social robots (spatial interaction, engagement detection, non-verbal interaction, verbal interaction, emotion generation and detection), research methods (study design principles, statistical tools for analyses) and have seen state-of-the-art research topics in the field including social learning, theory of mind, trust and ethical considerations in HRI.
Thanks to the exercise sessions and assignments, students gain first-hand knowledge and can independently apply techniques related to the above theory items, including for collecting stakeholders’ feedback for a robot design, programming the robot’s social behaviour along multiple modalities, extracting relevant user information from available sensors, designing and analysing HRI experiments.
The lectures cover all foundational topics in HRI (design principles and methodologies, human factors influencing HRI, sensors, actuators and software architecture for social robotics), challenges and solutions for key abilities of social robots (spatial interaction, engagement detection, non-verbal interaction, verbal interaction, emotion generation and detection), research methods (study design principles, statistical tools for analyses) and state-of-the-art topics including social learning, theory of mind and ethical considerations in HRI.
In the exercise sessions and related assignments students can experience first-hand how the theoretical concepts seen in the lectures can be applied in practice and learn how to collect stakeholders’ feedback for a robot design, program the robot’s social behaviour along multiple modalities, extract relevant user information from available sensors, design and analyse HRI experiments. At the end of the course, the students have a solid understanding of HRI, its principles, challenges and solutions and can autonomously apply such knowledge in practical contexts.
Course workload:
1) Attendance of the course: 22.5h (15x90min slots)
2) Attendance of the exercise sessions: 22.5h (15x90min slots)
3) Self-study of course material and work on homework assignments: 60h (4h/week)
4) Preparation for the exam: 80h
Knowledge of the content of modules Robotics I - Introduction to Robotics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114192 | Human-Computer-Interaction | ST | 6 | Beigl |
| T-INFO-114193 | Human-Computer-Interaction Pass | ST | 0 | Beigl |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
After completing the course, students will be able to
- reproduce basic knowledge about the field of human-machine interaction
- name and apply basic techniques for analysing user interfaces
- apply basic rules and techniques for designing user interfaces
- analyse and evaluate existing user interfaces and their function
Topics are:
1. human information processing (models, physiological and psychological principles, human senses, action processes),
2. design principles and design methods, input and output units for computers, embedded systems and mobile devices,
3. principles, guidelines and standards for the design of user interfaces
4. technical basics and examples for the design of user interfaces (text dialogues and forms, menu systems, graphical interfaces, interfaces in the WWW, audio dialogue systems, haptic interaction, gestures),
5. methods for modelling user interfaces (abstract description of interaction, embedding in requirements analysis and the software design process),
6. evaluation of systems for human-machine interaction (tools, evaluation methods, performance measurement, checklists).
7. practising the above basics using practical examples and developing independent, new and alternative user interfaces.
The total workload for this course unit is approx. 180 hours (6.0 credits).
Attendance time: Attendance of the lecture 15 x 90 min = 22 h 30 min
Attendance time: Attendance of the exercise 8 x 90 min = 12 h 00 min
Preparation / follow-up of the lecture 15 x 150 min = 37 h 30 min
Preparation / follow-up of the exercise 8x 360min =48h 00min
Go through slides/script 2x 2 x 12 h =24 h 00 min
Prepare exam = 36 h 00 min
SUM = 180h 00 min
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113395 | Humanoid Robots - Locomotion and Whole-Body Control | WT+ST | 6 | Mombaur |
| T-INFO-114282 | Humanoid Robots - Locomotion and Whole-Body Control -Pass | WT+ST | 0 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
By the end of the course, students will be able to:
• Develop kinematic and dynamic models of humanoid robots
• Understand basic principles of human whole-body movement
• Control gaits and other whole-body motions for humanoid robots and maintain balance
• Explain advanced methods for humanoid motion generation, optimization, and learning
• Give an overview of the state of the art in locomotion and whole-body control of humanoid robotics
• Complete a graduate level research project on humanoid robots including simulation and real-robot implementation
This course introduces fundamentals and recent developments in the field of humanoid robotics with a focus on locomotion and whole-body motions. We will cover kinematic and dynamic modeling of anthropomorphic systems, basic concepts of bipedal walking control, stability aspects, gait generation in different terrains, humanoid balance and push recovery, motion primitives and optimal control-based approaches, motion imitation and learning. The course will also give some insights in basic principles of passive dynamic walking, human motion generation and control and human motion modeling. Students will work with different robotics tools and perform a graduate level research project related to a whole-body humanoid robot.
This module is complementary to the course “4.290 Robotik II - Humanoide Robotik” which focuses on upper body motions and cognitive architectures while this course focuses on the specific aspects of legged humanoids and whole-body motions. The modules can be taken at the same time.
See partial achievements (Teilleistung)
For capacity reasons (supervision of computational projects) , the lecture is limited to 30 students. If necessary, a selection process will be carried out. Places will be allocated taking into account the students' subject-specific skills.
Estimated effort for this module is 180 hours:
60h - Lecture and exercises (2+2 SWS)
40h - Repetition of lecture contents, preparation of assignments
80h – Work on final project, documentation and presentation
Attendance of the lectures Robotics I - Introduction to Robotics and Mechano-Informatics in Robotics is required.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114170 | Humanoid Robots - Seminar | WT | 3 | Asfour |
See partial Achievements (Teilleistung)
See partial Achievements (Teilleistung)
The students choose a topic from the field of humanoid robotics, e.g. robot design, motion generation, perception or learning. They conduct a literature research on this topic under the guidance of a scientific supervisor. At the end of the semester, they present the results and write a term paper in English in the form of a scientific publication.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The student gained experience with literature research on a current research topic. He/she explored, understood and compared different approaches to a selected scientific problem. The student is able to write a summary of their literature research in the form of a scientific publication in English and to give a scientific talk on it.
Seminar with 2 SWS, 3 LP
3 LP corresponds to 90 hours, including
45 hours literature research
25 hours manuscript preparation
10 hours preparation of the presentation
10 hours attendance time
Attending the lectures Robotics I – Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III – Sensors and Perception in Robotics, Mechano-Informatics and Robotics and Wearable Robotic Technologies is recommended.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Interdisciplinary Qualifications
|
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Learning objectives can be divided into three main categories, which complement each other:
1. Orientation knowledge
2. practical orientation
3. basic skills
The House of Competence (HoC) is the central, research-based institution in the field of interdisciplinary competence development at KIT and offers students of all disciplines a broad learning portfolio. The HoC seminar program is divided into focus areas that aim to develop interdisciplinary skills for studies and careers. The three HoC laboratories are mainly responsible for the focus areas: the Methods LAB, Learning LAB and Writing LAB.
The courses in the HoC program can be credited in the areas of "Key Qualifications" (SQ), "Additional Professional Qualifications" (BOZ) and in the "Personal Competence Module" for student teachers (MPK). The requirements for the respective degree programs can be found in the applicable examination and study regulations. The current seminar program, which is published each semester, can be found on the HoC homepage at www.hoc.kit.edu.
German courses and/or language courses in the native language are not recognized as key qualifications.
Only those examination and study achievements can be credited,
which cannot be taken in the computer science or supplementary subjects.
Certificates of attendance are not accepted.
Each credit point corresponds to approx. 25-30 hours of work (by the student). This is based on the average student who achieves an average performance. The workload includes (for a lecture)
1. Attendance time in lectures, exercises
2. Preparation and follow-up of the same
3. Exam preparation and attendance in the same.
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
(Usage until 3/31/2027)
Elective Studies in Informatics (Usage until 3/31/2027) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101337 | Internet of Everything | WT | 4 | Zitterbart |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
Students know the platforms and applications of the Internet of Everything. Students have an understanding the challenges of designing protocols and applications for the IoE.
Students know and understand the risks to the privacy of users of the future IoE. They know protocols and mechanisms to enable future applications, such as smart metering and smart traffic, while protecting the privacy of users.
Students know and understand classic sensor network protocols and applications, such as media access procedures, routing protocols, transport protocols and mechanisms for topology control. Students know and understand the interaction of individual communication layers and the influence on, for example, the energy requirements of the systems.
Students know protocols for the Internet of Things such as 6LoWPAN, RPL, CoAP and DICE. Students understand the challenges and assumptions that have led to the standardization of protocols.
Students have a basic understanding of security technologies in IoE. They know typical protection goals and attacks, as well as building blocks and protocols to implement the protection goals.
The lecture deals with selected protocols, architectures, procedures and algorithms that are essential for IoE. In addition to classic topics from the field of wireless sensor-actuator networks, such as media access and routing, this also includes new challenges and solutions for the security and privacy of transmitted data in IoE. Socially and legally relevant aspects are also addressed.
Offered for the last time in WS 26/27!!!!!
Lecture with 2 SWS plus follow-up/exam preparation, 4 CP.
4 CP corresponds to approx. 120 working hours, of which
approx. 30 hours lecture attendance
approx. 60 hours preparation/follow-up work
approx. 30 hours exam preparation
See partial achievements (Teilleistung)
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| Prerequisite for: |
T-INFO-101287 - Seminar: Hot Topics in Bioinformatics T-INFO-103009 - Hands-on Bioinformatics Practical |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101286 | Introduction to Bioinformatics for Computer Scientists | WT | 3 | Stamatakis |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students attain comprehensive knowledge of standard methods, algorithms, theoretical principles and open problems in the field of sequence-based bioinformatics (biological principles, sequence assembly, pairwise sequence alignment, multiple sequence alignment, phylogenetic tree reconstruction under parsimony, likelihood and Bayesian models, coalescent inference in population genetics). They develop the ability to categorise and evaluate algorithms and problems. They can select suitable models and methods for a given biological data analysis problem and can justify their choice. Students will be able to design analysis pipelines for biological data analysis.
Initially, some basic concepts and mechanisms of biology are introduced. Subsequently, algorithms and models from the fields of sequence analysis (sequence alignment, dynamic programming, sequence assembly), population genetics, and discrete as well as numerical algorithms for inferring molecular phylogenetic trees (parsimony, likelihood, Bayesian inference) are discussed. Furthermore, discrete operations on trees are treated (e.g., topological distances between trees, consensus tree algorithms). A substantial part of the lectures will cover the practical implementation, the optimisation, and the parallelisation of the respective methods.
2 SWS lecture + 1.5 * 2 SWS follow-up) * 15 + 15 hours exam preparation = 90 hours = 3 ECTS
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113960 | IT Security | WT | 6 | Müller-Quade, Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
• have in-depth knowledge of cryptography and IT security
• know and understands sophisticated techniques and security primitives to achieve the protection goals
• know and understand scientific evaluation and analysis methods of IT security (game-based formalization of confidentiality and integrity, security and anonymity notions)
• have a good understanding of types of data, personal data, legal and technical fundamentals of privacy protection
• know and understand the fundamentals of system security (buffer overflow, return-oriented programming, ...)
• know different mechanisms for anonymous communication (TOR, Nym, ANON) and can assess their effectivity
This advanced mandatory module deepens different topics of IT security. These include in particular:
• Elliptic curve cryptography
• Threshold cryptography
• Zero-knowledge proofs
• Secret sharing
• Secure multi-party computation and homomorphic encryption
• Methods of IT security (game-based analysis and the UC model)
• Crypto-currencies and consensus through proof-of-work/stake
• Anonymity on the Internet, anonymity with online payments
• Privacy-preserving machine learning
• Security of machine learning
• System security and exploits
• Threat modeling and quantification of IT security
Course workload:
1. Attendance time: 56 h
2. Self-study: 56 h
3. Preparation for the exam: 68 h
Attendance of the lecture Information Security is recommended.
Literature:
• Katz/Lindell: Introduction to Modern Cryptography (Chapman & Hall)
• Schäfer/Roßberg: Netzsicherheit (dpunkt)
• Anderson: Security Engineering (Wiley, and online)
• Stallings/Brown: Computer Security (Pearson)
• Pfleeger, Pfleeger, Margulies: Security in Computing (Prentice Hall)
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114205 | Lab Project: Speech Translation | WT | 6 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student
- is able to develop a language translation system using state-of-the-art methods.
- can evaluate language translation systems.
- can present his/her findings in a scientific lecture.
The use of deep learning technologies has significantly improved the quality of machine translation of text and speech in recent years. In this internship, students will develop a language translation system for a new language pair using state-of-the-art methods.
In the first part of the internship, students are introduced step-by-step to the development of a translation system and its evaluation. To this end, the various subtasks must be solved. In the second part of the internship, the students are asked to independently analyse various improvements to the system.
180h
Approx. 15h presence
Approx. 15h pre/post processing
Approx. 140h self-study
Approx. 10h Preparation of scientific presentation
Students should have understood the theoretical principles as introduced in the lectures Deep Learning or Machine Translation.
| Coordinators: |
Prof. Dr. Achim Streit
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115215 | lntroduction to Quantum Machine Learning | ST | 3 | Kühn |
see partial achievement
Students have a systematic understanding of the issues surrounding the use of currently available quantum computers and of solution approaches from the field of hybrid quantum machine learning. They will be able to transfer these findings to other problems and, in particular, evaluate the efficiency and feasibility for different data sets in practice. In addition, they will be able to interpret and understand current research results in quantum machine learning with the knowledge you have acquired.
After successfully completing the course, students will be able to
This module aims to familiarize students with the theoretical and practical aspects of the hybrid use of quantum circuits in classical machine learning algorithms. In the first part of the lecture, the necessary mathematical foundations of quantum systems and their representation by qubits and quantum circuits are summarized before the advantages and possibilities of quantum computing are demonstrated on the basis of known quantum algorithms. Finally, an overview of current hybrid approaches in the field of quantum machine learning (OML) and their possible applications and limitations is provided:
The module focuses in particular on the applicability to today's quantum computers and the scalability of the approaches presented.
- Lecture attendance: 23h (2 SWS x 15)
- Preparation and follow-up: 45h (2 x 2 SWS x 15)
- Exam preparation: 22h
- Total: 90h / 30 = 3 credits
- Attendance of the lecture "Introduction to Quantum Computing" is recommended
- Knowledge of linear algebra is recommended
- Programming skills in Python are helpful
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 4/1/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 4/1/2026) Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115014 | Localization of Mobile Agents | ST | 6 | Hanebeck |
| T-INFO-115015 | Localization of Mobile Agents - Pass | ST | 0 | Hanebeck |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Participants in this course will gain a broad overview of probabilistic machine learning tools and learn how to creatively combine them to solve a wide variety of problems.
This module provides a systematic introduction to the field of localization methods. To facilitate entry, the module is divided into four central topics. Dead reckoning (1) deals with the step-by-step determination of a vehicle's position from dynamic parameters such as speed or steering angle, as well as intertial measurement unit (IMU) data. Localization with the aid of measurements to external, known landmarks is part of static localization (2). Specifically, signal times of arrival (multilateration and GPS), distance measurements, and angle measurements (bearings) are covered. In addition to closed-form solutions for special measurements, the method of least squares for fusing arbitrary measurements is introduced. This also includes nonlinear optimization methods and certifiable global optimization. Dynamic localization (3) deals with the combination of dead reckoning and static localization using the Kalman filter, which is used in virtually all object tracking scenarios and other state estimation problems. General estimator properties are also discussed. The course concludes with simultaneous localization and mapping (SLAM) (4), which allows localization even when the location of landmarks is partially unknown. Thereby the measurement association problem is also addressed.
Per week:
2 SWS Presence
2h Follow-up
6h Digital exercise with computation tasks
2h exam preparation
= 12h/week und 180h/semester
Knowledge of a higher-level programming language with mature libraries for scientific and numerical computing (e.g., Julia, Matlab, Python) is an advantage.
| Organisation: |
KIT Department of Informatics |
|---|---|
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2026)
Area of Specialization: Software Engineering and Compiler Construction (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115318 | Logic Programming | Irreg. | 6 | Erdweg |
Logic programs consist of logical implications that are executed to derive complex conclusions from initial knowledge. Logic programming has applications in many different areas such as network modeling, symbolic artificial intelligence, and source-code analysis. This course introduces foundational and advanced topics of logic programming, including: logical foundations, unification, denotational and operational semantics, evaluation strategies, negation as failure, Datalog, Prolog, and incremental resolution.
1. Attendance time 4 h x 15 weeks = 60 h
2. Self-study 7 h x 15 weeks = 105 h
3. Preparation for the exam 15 h
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Software Engineering and Compiler Construction Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112360 | Logical Foundations of Cyber-Physical Systems | WT | 6 | Platzer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
In modeling and control, successful students will
– understand core principles behind CPS. A solid understanding of these principles is important for anyone who wants to integrate cyber and physical components to solve problems that no part could solve alone.
– develop models and controls. In order to understand, design, and analyze CPS, it is important to be able to develop models for the relevant aspects of a CPS design and to design controllers for the intended functionalities based on appropriate specifications, including modeling with differential equations.
– identify relevant dynamical aspects. It is important to be able to identify which types of phenomena influence a property of a system. These allow us to judge, for example, where it is important to manage adversarial effects, or where a nondeterministic model is sufficient.
In computational thinking, successful students should be able to
– identify safety specifications and critical properties. In order to develop correct CPS designs, it is important to identify what “correctness” means, how a design may fail to be correct, and how to make it correct.
– understand abstraction in system designs. The power of abstraction is essential for the modular organization of CPS, and the ability to reason about separate parts of a system independently.
– express pre- and post-conditions and invariants for CPS models. Pre- and post-conditions allow us to capture under which circumstance it is safe to run a CPS or a part of a CPS design, and what safety entails. They allow us to achieve what abstraction and hierarchies achieve at the system level: decompose correctness of a full CPS into correctness of smaller pieces. Invariants achieve a similar decomposition by establishing which relations of variables remain true no matter how long and how often the CPS runs.
– reason rigorously about CPS models. Reasoning is required to ensure correctness and find flaws in CPS designs. Both informal and formal reasoning in a logic are important objectives for being able to establish correctness, which includes rigorous reasoning about differential equations.
In CPS skills, successful students will be able to
– understand the semantics of a CPS model. What may be easy in a classical isolated program becomes very demanding when that program interfaces with effects in the physical world.
– develop an intuition for operational effects. Intuition for the joint operational effect of a CPS is crucial, e.g., about what the effect of a particular discrete computer control algorithm on a continuous plant will be.
– understand opportunities and challenges in CPS and verification. While the beneficial prospects of CPS for society are substantial, it is crucial to also develop an understanding of their inherent challenges and of approaches for minimizing the impact of potential safety hazards. Likewise, it is important to understand the ways in which formal verification can best help improve the safety of system designs.
Cyber-physical systems (CPSs) combine cyber capabilities (computation and/or communication) with physical capabilities (motion or other physical processes). Cars, aircraft, and robots are prime examples, because they move physically in space in a way that is determined by discrete computerized control algorithms. Designing these algorithms to control CPSs is challenging due to their tight coupling with physical behavior. At the same time, it is vital that these algorithms be correct, since we rely on CPSs for safety-critical tasks like keeping aircraft from colliding. In this course we will strive to answer the fundamental question posed by Jeannette Wing:
“How can we provide people with cyber-physical systems they can bet their lives on?”
The cornerstone of this course design are hybrid programs (HPs), which capture relevant dynamical aspects of CPSs in a simple programming language with a simple semantics. One important aspect of HPs is that they directly allow the programmer to refer to real-valued variables representing real quantities and specify their dynamics as part of the HP.
This course will give you the required skills to formally analyze the CPSs that are all around us—from power plants to pacemakers and everything in between—so that when you contribute to the design of a CPS, you are able to understand important safety-critical aspects and feel confident designing and analyzing system models. It will provide an excellent foundation for students who seek industry positions and for students interested in pursuing research.
Course web page: https://lfcps.org/course/lfcps.html
6 ECTS from 180h of coursework consisting of
45h = 15 * 3 from 3 SWS lectures
15h = 15 * 1 from 1 SWS exercises
68h preparation, reading textbook, studying
40h solving exercises
12h exam preparation
The course assumes prior exposure to basic computer programming and mathematical reasoning. This course covers the basic required mathematical and logical background of cyber-physical systems. You will be expected to follow the textbook as needed: André Platzer. Logical Foundations of Cyber-Physical Systems. Springer 2018. DOI:10.1007/978-3-319-63588-0
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101344 | Low Power Design | ST | 3 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are made aware of various low power design optimizations employed in state-of-the-art embedded devices. This involves optimization techniques that incorporate embedded machine learning algorithms to enhance system performance. At the end of the lecture, the students will be able to recognize the challenges involved in crafting efficient low power designs and how to tackle them.
The lecture provides an overview of design methods, synthesis tools, estimation models, software techniques, operating system strategies, scheduling algorithms, embedded machine learning methods, etc., with the aim of minimizing the power consumption of embedded devices without compromising their performance. Both the research-relevant and industry-prevalent topics at different level of abstractions (from circuit to system) are discussed in this lecture.
Attendance time: 30 hours (2 SWS × 15 weeks)
Self-study: 45 hours (1.5 × 2 SWS × 15 weeks)
Exam preparation: 15 hours
Total: 90 hours (3 ECTS)
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114197 | Machine Learning - Foundations and Algorithms | ST | 6 | Neumann |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
• Students acquire knowledge of the basic methods of Machine Learning
• Students acquire the mathematical knowledge to understand the theoretical foundations of Machine Learning
• Students can categorize, formally describe and evaluate methods of Machine Learning
• Students can apply their knowledge to select appropriate models and methods for selected problems in the field of Machine Learning.
The field of Machine Learning has made enormous progress in recent years and good knowledge of Machine Learning is becoming increasingly in demand on the job market. Machine Learning describes the acquisition of knowledge by an artificial system based on experience or data. Rules or certain calculations no longer have to be manually coded but can be extracted from data by intelligent systems.
This lecture provides an overview of essential and current methods of Machine Learning. After reviewing the necessary mathematical background, the lecture primarily deals with algorithms for classification, regression, and density estimation, with a focus on the mathematical understanding of probabilistic methods and neural networks.
Examples of topics include:
- Basics in Linear Algebra, Probability Theory, Optimization and Constraint Optimization
- Linear Regression
- Linear Classification
- Model Selection, Overfitting, and Regularization
- Support Vector Machines
- Kernel Methods
- Bayesian Learning and Gaussian Processes
- Neural Networks
- Dimensionality Reduction
- Density estimation
- Clustering
- Expectation Maximization
- Graphical Models
180h, divided into:
- ca 45h lecture attendance
- approx. 15h attending exercises
- approx. 90h post-processing and working on the exercise sheets
- ca 30h exam preparation
| Coordinators: |
Prof. Dr. Pascal Friederich
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Area of Specialization: Data Science Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113916 | Machine Learning for Natural Sciences | ST | 3 | Friederich |
| T-INFO-113917 | Machine Learning for Natural Sciences - Pass | ST | 3 | Friederich |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
Qualification Objectives
• Students are able to name relevant machine learning methods, describe them, as well as develop independent proposals on how questions in the natural sciences and material sciences can be answered using machine learning methods.
Learning Objectives
• Necessary knowledge for the selection and, if necessary, the adaptation of suitable machine learning models.
• Knowledge about data availability and, if necessary, planning of training data generation
• Knowledge of practical implementation, training, and systematic evaluation of machine learning models in python using common libraries (sklearn, TensorFlow, Keras, PyTorch, etc.)
• Knowledge of ways and means to systematically analyze and interpret results.
This module covers the theoretical and practical aspects of machine learning methods and their application to problems in natural sciences, especially in materials science and chemistry. Students gain insight into machine learning fundamentals as well as current research topics of this still young interdisciplinary field. Topics covered include the application of machine learning methods for medical image analysis, sequence analysis and generation, the prediction of material and molecular properties, generative models for materials design, Bayesian methods for decision making in autonomous experiments, as well as interpretation possibilities of all methods for gaining scientific understanding.
A practical exercise based on jupyter notebooks gives students insight into the practical aspects of machine learning for natural sciences and supports the learning process.
4 SWS: (2 SWS Lecture + 2 SWS Exercise + 1,5 x 4 SWS Preparation) x 15 + 30 h exam preparation
= 180 h
• Knowledge of the basics of machine learning is helpful but not required
• Interest in natural science topics is required
• Basic knowledge of python is recommended. It has to be acquired during the semester through self-study
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Area of Specialization: Data Science Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113083 | Machine Learning in Climate and Environmental Sciences | ST | 6 | Nowack |
| T-INFO-113085 | Machine Learning in Climate and Environmental Sciences - Pass | ST | 0 | Nowack |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Learning objectives:
Students will be able to effectively address complex data science challenges. They can design and use robust strategies/modelling pipelines for machine learning applications in the climate and environmental sciences, which are transferable to other disciplines.
Their acquired knowledge will include major classes of machine learning techniques, how to choose and differentiate among algorithms in a variety of problem settings, ways of assessing important data properties that could for example help or interfere with modelling goals, and methods to combine data-driven modelling with prior scientific system understanding to increase performance and trustworthiness of machine learning.
Students will learn how to implement these approaches in Python, using major machine learning software packages.
This module covers key concepts for real-world applications of machine learning, focusing on environmental data science. These include:
• foundations of machine learning (e.g., curse of dimensionality, cross-validation, cost functions, feature engineering)
• several widely applied regression, classification, and unsupervised learning algorithms (e.g., LASSO, random forests, Gaussian processes, neural networks, LSTMs, transformers, self-organizing maps)
• time series forecasting and causal inference.
• explainable AI (e.g., SHAP value analyses, feature permutation methods, intrinsically interpretable methods).
These concepts will be discussed in applied contexts, using current research examples from the climate and environmental sciences, including: climate change modelling, machine learning emulation of numerical models, forecasting air pollution and wildfires, understanding coupled dynamical systems such as global teleconnections in climate science, challenges in modelling non-stationary systems (e.g., predicting extreme weather events under global warming), and anomaly detection in measurement data.
The lectures are accompanied by computer exercises in which students learn how to implement and modify machine learning modelling pipelines first-hand.
Concerning in-person events, this is a 4 SWS module: 2 SWS for lectures, 2 SWS for exercises
Overall:
(2 SWS lectures + 2 SWS exercises + 1.5 x 4 SWS preparation and homework) x 15 +30 h preparation for the exam = 180 h = 6 ECTS
• Previous programming experience, e.g. in scientific contexts or in computer science, is required.
• Knowledge of fundamentals about machine learning is an advantage.
• Knowledge of the Python programming language is an advantage.
• Good knowledge of mathematical concepts such as linear algebra is an advantage.
• An interest in scientific questions important for the climate- and environmental sciences.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Master‘s Thesis
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113697 | Master's Thesis | WT+ST | 30 | Koziolek |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The workload for the module is approximatly 900 hours.
| Coordinators: |
Prof. Dr. Oliver Waldhorst
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114271 | Mobile Communication | WT | 4 | Waldhorst, Zitterbart |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
- know the basic concepts of mobile communication and can evaluate basic methods and influencing factors of wireless communication
- are familiar with the structure and functionality of prominent, practically relevant mobile communication systems (e.g. GSM, UMTS, WLAN)
- know typical problems in mobile communication systems and can evaluate, select and apply suitable methods to solve them
Students are familiar with typical problems in wireless transmission (e.g. signal propagation, attenuation) and can explain these using examples and relate them to each other. They can also recognize where these problems typically occur when designing different communication systems.
Students are familiar with a portfolio of methods for modulating digital data, multiplexing, coordinating competing media access and mobility management. They will be able to explain these in their own words, evaluate them and select suitable candidates when designing mobile communication systems.
Students master the basic concepts of wireless local networks according to IEEE 802.11 and wireless personal networks with Bluetooth. They can explain these and compare the respective variants with each other. They will also be able to analyze and evaluate media access in detail.
Students master the structure of digital telecommunications systems such as GSM, UMTS and LTE as well as the individual tasks of the respective components and their detailed interaction in the overall system. They are familiar with the conceptual differences between the systems presented and can explain in their own words why certain methods from the portfolio are used in the respective systems.
Students will be familiar with basic routing methods in self-organizing wireless ad hoc networks and will be able to analyse these comprehensively and evaluate their use depending on the application scenario. Furthermore, they master the basic concepts of mobility support on the Internet (Mobile IP and Mobile IPv6).
The lecture first discusses typical problems in wireless transmission, such as signal propagation, attenuation, reflections and interference. Based on this, it develops a portfolio of methods for modulation of digital data, multiplexing, coordination of competing media accesses and mobility management. To illustrate where and how these methods are used in practice, typical mobile communication systems of great practical relevance are presented in detail. These include wireless local area networks according to IEEE 802.11, wireless personal networks with Bluetooth as well as wireless telecommunication systems such as GSM, UMTS with HSPA and LTE. Discussions of mechanisms at the network layer (mobile ad-hoc networks and MobileIP) and transport layer round off the lecture.
Lecture with 2 SWS plus follow-up/exam preparation, 4 CP.
4 CP corresponds to approx. 120 working hours, of which
approx. 30 hours lecture attendance
approx. 60 hours preparation/follow-up work
approx. 30 hours exam preparation
| Coordinators: |
Prof. Dr.-Ing. Peter Rost
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-112127 | Mobile Communications | WT | 4 | Rost |
The success control takes place in the form of an oral examination lasting approx. 25 minutes. Before the examination, there is a preparation phase of 15 minutes in which preparatory tasks are solved.
none
Students are enabled to analyze and assess functionalities of mobile communication systems. They learn how to apply and implement fundamental methods of the lecture “Communications Engineering I” in mobile radio networks. Furthermore, students will be enabled to understand requirements and limitations of mobile applications.
At the beginning, this course describes exemplary applications of mobile communications and elaborates on resulting requirements. Based on a solid understanding of those requirements, selected approaches and techniques will be presented that are solving the respective challenges in mobile communication systems. To this end, algorithms as well as system architectures are discussed in order to acquire solid knowledge on the radio network, the core network and the integration with applications and services.
Grade of the module corresponds to the grade of the oral exam.
In total: 120 h = 4 LP
Knowledge of basic engineering as well as basic knowledge of communications engineering and Previous attendance of the lecture "Communication Engineering I" is recommended. Sound English language skills are required.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113896 | Model-Driven Software Development | WT | 3 | Burger, Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
* Students understand model-driven approaches for software and systems development, and they can use and assess them.
* Students can create metamodels and transformations using established model-driven development processes and standards of the OMG (MOF, QVT, XMI, UML, etc.), as well as state-of-the-art languages and tool} (Xtext, Xtend, Xpand, etc.)
* Students know the theoretical background of model transformation languages.
* Students know practical applications of model-driven technologies.
* Students can assess standards and technologies and can estimate their respective advantages and disadvantages.
Model-driven software development pursues the development of software systems on the basis of models. The models are not only used to document, design and analyse an initial system, as is usual in conventional software development, but rather serve as primary development artefacts from which the final system can be generated in its entirety if possible. This focus on models offers a number of advantages, such as an increase in the level of abstraction at which the system is specified, improved communication options that can extend to the end customer through domain-specific languages (DSL), and an increase in the efficiency of software development through automated transformations of the created models to the source code of the system. However, there are still some unresolved challenges in the use of model-driven software development, such as model versioning, evolution of DSLs, maintenance of transformations or the combination of teamwork and MDSD. Although MDSD is already used in practice due to the advantages mentioned, the challenges mentioned also offer opportunities for current research.
The lecture introduces concepts and techniques that are part of MDSD. As a basis, the systematic creation of meta-models and DSLs including all necessary components (concrete and abstract syntax, static and dynamic semantics) is introduced. This is followed by a general discussion of the concepts of transformation languages and an introduction to some selected transformation languages. The embedding of MDSD in the software development process provides the necessary foundations for their practical use. The remaining lectures deal with further issues such as model versioning, model coupling, MDSD standards, teamwork based on models, testing of model-driven software, as well as the maintenance and further development of models, meta-models and transformations. Finally, model-driven methods for analysing software architecture models are covered as an advanced unit. The lecture deepens concepts from existing courses such as software engineering or compiler construction or transfers and extends them to model-driven approaches. Furthermore, formal techniques are applied in transformation languages, such as graph grammars, logical calculi or relational algebrae.
(2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
Minor Studies: Mathematics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MATH-113911 | Modern Methods in Combinatorics | Irreg. | 6 | Aksenovich |
The module examination takes the form of an oral examination (approx. 30 min).
None
Students are able to name, explain, and apply modern methods of combinatorics. In particular, they are capable of applying probabilistic techniques, the polynomial method, the container method, and regularization techniques to combinatorial problems. Furthermore, students are able to communicate using technical English
The course is concerned with modern methods in Combinatorics including probabilistic or algebraic ones. Every presented method is illustrated with several applications.
The probabilistic part includes the following topics: random graphs, linearity of expectation, second moment method, and Lovasz Local Lemma. The algebraic part includes: polynomial methods, spectral methods, and linear algebraic techniques.
The module grade is the grade of the oral examination.
Total workload: 180 hours
Attendance time: 60 hours
Self-study: 120 hours
Some knowledge of linear algebra and probability theory is strongly recommended. The courses Graph Theory and Combinatorics are recommended but not required.
Lecture + Tutorial (3+1 weekly contact hours)
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-105140 | Motion in Human and Machine - Seminar | ST | 3 | Asfour |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students understand methods for modeling, processing, and analyzing human motions. They can explain approaches for capturing biological motion and biomechanical simulation.
Students can describe methods for learning motion primitives from human demonstrations and understand how human motion is mapped to robots with different kinematics and dynamics. They can apply these methods in new contexts.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
This interdisciplinary block seminar addresses methods for modeling, generating, and controlling movements in human and robot systems. Students gain insight into this interdisciplinary field, learning fundamentals of capturing biological motion, biomechanical simulation, robotics, and machine learning.
The seminar begins with the generation of human movement as an effect of muscle contraction. It covers how observed movement patterns can be identified and categorized. Methods for learning movement primitives from human demonstrations are presented, including approaches for reproducing these patterns on humanoid robots. The seminar addresses the challenge of mapping human motion to robots with different kinematics and dynamics. Students learn how motion primitives enable transfer of human movements to robotic systems and their application to motion generation in humanoid robots.
Students work in small groups on collaborative projects that apply the learned methods. These projects allow students to deepen their understanding through practical application of the methods.
This interdisciplinary seminar is conducted in cooperation with the University of Stuttgart and the University of Tübingen.
Seminar with 3 SWS, 3 LP
3 LP corresponds to 90 hours, including
30 hours attendance time
15 hours group work
20 hours literature research
20 hours manuscript preparation
5 hours video creation
Programming experience in C++, Python or Matlab is recommended.
Attending the lectures Robotics I – Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics, Mechano-Informatics and Robotics and Wearable Robotic Technologies is recommended.
| Coordinators: |
Prof. Dr. Jan Niehues
Prof. Dr.-Ing. Rainer Stiefelhagen
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115041 | Multimodal Artificial Intelligence | ST | 6 | Niehues, Stiefelhagen |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Due to the successes in research, multimodal AI systems are increasingly integrated into our everyday lives. These are, for example, systems that can understand and generate language and speech or analyze images and videos. Furthermore, advanced video and image generation AI shows leads to new applications
Based on the knowledge of the lecture “Introduction to AI”, the students learn to understand, develop and evaluate multimodal AI Systems.
In order to bring this knowledge closer to the students, the lecture is divided into 5 parts. First, the lecture investigates method of speech and language perception followed by a part about visual perception. The second part deals speech and language generation followed by a part about video processing. Finally, multimodal AI methods are presented.
Lecture with 3 SWS + 1 SWS exercise , 6 CP.
6 LP corresponds to approx. 180 hours, of which
approx. 45 hours lecture attendance
approx. 15 hours exercise visit
approx. 90 hours post-processing and processing of the exercise sheets (self-study)
approx. 30 hours exam preparation (self-study)
See Partial Achievements (Teilleistung).
| Coordinators: |
Prof. Dr. Sebastian Kempf
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-111232 | Nano- and Quantum Electronics | ST | 6 | Kempf |
The assessment of success takes place in the form of a written examination lasting 120min. The grade corresponds to the result of the written examination.
none
Students will understand the physical limits of CMOS scaling and will be able to analyze the function of conventional nanoelectronic devices. Students will also understand the operation of novel nanoelectronic and quantum electronic devices and will be able to design this kind of devices that are based on quantum mechanical effects. They develop the ability to design nanoelectronic sensors and devices and can understand and analyze the fabrication methods for nano- and quantum electronic devices.
Nanoelectronics deals with integrated circuits whose typical length scale is well below 100nm. In this regime, physical effects, in particular of quantum mechanical origin, occur and strongly influence the scaling of classical microelectronic devices. This ultimately leads to a new form of electronic components as well as novel operation principles. A special form of nanoelectronics is quantum electronics in which quantum mechanical effects are exploited on purpose to build an entirely new class of devices whose performance reaches far beyond any other microelectronics devices. Well-known examples are superconducting digital electronics which enables to build, for example, microprocessors with clock rates exceeding several 100GHz, or the quantum computer, which will lead to a change of paradigms in the field of information processing.
Within this context, the module "Nano- and quantum electronics" intends to give students an overview of the theoretical and practical aspects of nano- and quantum electronics. In particular, it discusses the following topics:
The tutorial is closely linked to the lecture and deals with special aspects concerning the development of nano- and quantum electronics. In particular, the development and system integration of such devices for various applications is discussed by means of exercises.
The module grade is the grade of the written examination.
A workload of approx. 175h is required for the successful completion of the module. This is composed as follows:
Successful completion of the modules "Superconductivity for Engineers" and „Einführung in die Quantentheorie für Elektrotechniker“ is recommended.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114207 | Natural Language Processing | WT | 6 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- To familiarise the student with the problems that exist in natural language processing
- The student should be introduced to the basic techniques for solving the problems.
- The student should gain an insight into current research in the field of natural language processing and be able to use the language processing and can use the acquired knowledge to work on current research topics
Summarise today's lecture? When were neural networks invented? Artificial intelligence that can answer these questions has long been a dream of mankind. And today we are seeing the first programmes that can solve these problems. In this lecture we will provide the skills and knowledge to develop solutions to these problems of natural language processing using state-of-the-art methods.
After an introduction to the challenges of natural language processing, the different tasks in natural language processing are discussed. One focus of the course is on methods from the field of deep learning. Firstly, sequence classification tasks such as sentiment analysis are covered. Next, methods of sequence labelling are discussed, such as those used in the recognition of proper names or the determination of part-of-speech tags. The lecture will then discuss sequence-to-sequence methods. These models are used in many natural language processing tasks, such as machine translation, automatic summarisation and automatic question answering.
In this course, the important challenges in the development of systems will be addressed: The representation of words, neural architectures to model language, methods to train complex models, and finding the most likely output.
180hLecture with 4 SWS, 6 CP.
6 LP corresponds to approx. 180 hours, of which
approx. 60 hours lecture attendance
approx. 90 hours post-processing (self-study)
approx. 30 hours exam preparation (self-study)
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114257 | Natural Language Processing and Software Engineering | WT | 3 | Koziolek |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know basic concepts of linguistics such as syntax, semantics and pragmatics, and can explain and compare them. They are familiar with lexical relations such as polysemy, homonymy, and troponymy and can identify relevant examples. Furthermore, they can identify and compare connections between the relations.
Students are familiar with basic concepts of computational linguistics. Basic techniques such as part-of-speech tagging, lemmatization, word similarities and disambiguation can be explained. Associated methods (lexical, rule-based, or probabilistic) can be described and their respective strengths and weaknesses assessed. Different parsing methods can be named, explained and conceptually reproduced.
Students can describe and compare the structure, content and benefits of different knowledge bases. In addition to the overarching concepts of ontology, lexical databases and other knowledge representations, they are also familiar with specific representatives, such as WordNet, DBpedia and similar, and can use them.
Students understand the connection between the functionality of basic computational linguistics techniques and their applicability in software engineering. In addition, they can break down tool chains into individual components and evaluate them. In particular, students will be able to analyze and evaluate different applications. These include automated modeling, improving requirements specifications, and traceability link recovery. In addition, students can explain the concept of large language models (LLMs) and their application and use in the field of language processing. Students can identify application scenarios in software engineering for text analysis systems and design their own solutions.
This lecture provides the basics for the automated processing of natural language texts. Language processing is becoming increasingly important.
Linguistic input plays a critical role in interactive systems, such as voice commands, assistance systems, and query interfaces. Additionally, the analysis and processing of text-based software artifacts represents an important field of research. Computational linguistics is therefore not only of great importance for software applications, but also for software engineering itself.
The aim of this lecture is to provide basic knowledge of natural language processing (NLP) and its potential applications in the development of software systems. Key topics include the automated analysis of texts, the challenges posed by the inherent ambiguity of natural language, the translation of natural language texts into software models, and the use of large language models (LLMs) in software engineering. The lecture will also explore current research developments and trends in the field.
3 ECTS correspond to approximately 90 hours of work, including:
approx. 30 hours of attending lectures
approx. 45 hours of preparation and follow-up work
approx. 15 hours of exam preparation
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
(Usage from 4/1/2025)
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114238 | Network Security: Architectures and Protocols | ST | 4 | Zitterbart |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
In particular, students are familiar with typical attack techniques such as eavesdropping, interception or replaying and can explain these using examples. In addition, students are familiar with cryptographic primitives such as symmetric and asymmetric encryption, digital signatures, message authentication codes and can apply these in particular for the design of secure communication services.
Students are familiar with the Kerberos distributed authentication service and can explain the protocol flow in their own words and name basic concepts (e.g. tickets). In addition, students are familiar with relevant communication protocols for protecting communication on the Internet (e.g. IPsec, TLS) and can explain these and analyze and evaluate their security properties.
Students know different methods for network access protection and can explain and compare common authentication methods (e.g. CHAP, PAP, EAP). Furthermore, students are proficient in methods for protecting wireless access networks and can analyze and evaluate methods such as WEP, WPA and WPA2.
Students master different trust models and can explain and apply basic technical concepts (e.g. digital certificates, PKI) in their own words. In addition, students develop an understanding of data protection aspects in communication networks and can explain and apply technical procedures to protect privacy.
The lecture "Network Security: Architectures and Protocols" looks at challenges and techniques in the design of secure communication protocols as well as data protection and privacy issues. Complex systems such as Kerberos are examined in detail and their design decisions with regard to security aspects are highlighted. Special focus is placed on PKI fundamentals, infrastructures and specific PKI formats. Further emphasis is placed on the common security protocols IPSec and TLS/SSL as well as protocols for infrastructure protection.
Lecture with 2 SWS plus follow-up/exam preparation, 4 CP.
4 CP corresponds to approx. 120 working hours, of which
approx. 30 hours lecture attendance
approx. 60 hours preparation/follow-up work
approx. 30 hours exam preparation
| Coordinators: |
Dr.-Ing. Roland Bless
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101321 | Next Generation Internet | ST | 4 | Bless, Zitterbart |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
- know the basic properties and architectural concepts of the Internet as well as its limitations- know newer transport protocols and current approaches to increase the flexibility of Internet-based communication and can apply this knowledge in practice.are familiar with concepts for quality of service support and group communications and can apply mechanisms for their implementation on the Internethave the ability to analyze and evaluate peer-to-peer systems and advanced routing protocolsare familiar with concepts of satellite networking and quantum Internet in particular,
- know important architectural concepts and design principles that are used on the Internet andcan explain these using examples or apply them themselves when designing systems. In addition, students know theconcept of quality of service and important quality of service parameters, are familiar with basic mechanisms for supportingquality of service (e.g. classifiers, traffic shapers, queuing and scheduling strategies, resource reservation), can analyze and evaluate them and can apply them to the design of communication systems.
- Moreover, students know the requirements and challenges for today's transport protocols and newer congestioncontrol algorithms and can analyze and assess trade-offs of the presented approaches.
- know the characteristics of peer-to-peer systems, can explain them and can compare different forms of organization.Furthermore, students master routing procedures in such decentrally organized peer-to-peer systems and can explainhow they work in detail in their own words.
- Similarly, students know inherent trade-offs for routing in the Internetand can explain newer approaches in their own words.
- In addition, students develop an understanding of the functioning of newer approaches to increase the flexibility ofcommunication networks (e.g. network virtualization, software-defined networking, service function chaining) andan analyze, explain, and apply technical procedures for their implementation.
- Moreover, students know properties of satellite and quantum networks and their corresponding challenges.
The lecture focuses on current developments in Internet-based network technologies. First, architectural principles of today's Internet are presented and discussed, subsequently nowadays and future challenges are motivated.
The lecture also discusses approaches and paradigms beyond the current Internet architecture, methods for quality-of-service support, newer transport protocols and congestion control approaches as well as group communication support. Deployments of the presented technologies in IP-based networks are discussed. The lecture presents advanced approaches such as programmable networks, network virtualization as well as newer approaches and protocols for routing, satellite networking, and peer-to-peer networks. A brief introduction to
the technology of a future quantum Internet is provided as well.
Lecture with 2 SWS plus follow-up/exam preparation, 4 CP.
4 CP corresponds to approx. 120 working hours, of which
approx. 30 hours lecture attendance
approx. 60 hours preparation/follow-up work
approx. 30 hours exam preparation
J.F. Kurose, K.W. Ross; Computer Networking: A Top-Down Approach. Pearson, 2022, 8th Edition, ISBN 978-1292405469
| Coordinators: |
Prof. Dr. Wilhelm Stork
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-100676 | Optical Engineering | WT | 4 | Stork |
Achievement will be examined in an oral examination (approx. 20 minutes).
none
The students from different backgrounds refresh and elaborate their knowledge of engineering optics and photonics. They will get to know the basic principles of optical designs. They will connect these principles with real-world applications and learn about their problems and how to solve them. The students will know about the human view ability and the eye system. After the module they will be able to judge the basic qualities of an optical system by its quantitative data.
After the course, students will:
The course "Optical Engineering" teaches the practical aspects of designing optical components and instruments such as lenses, microscopes, optical sensors and measurement systems, and optical disc systems (e.g. CD, DVD, HVD). The course explains the layout of modern optical systems and gives an overview over available technology, materials, costs, design methods, as well as optical design software. The lectures will be given in the form of presentations and accompanied by individual and group exercises. The topics of the lectures include:
I. Introduction (Optical Phenomena)
II. Ray Optics (thin/thick lenses, principal planes, ABCD-matrices, chief rays, examples: Eye, IOL)
III. Popular Applications (Magnifying glass, microscope, telescope, Time-of-flight)
IV. Wave Optics (Interference, Diffraction, Spectrometers, LDV)
V. Aberrations I (Coma, defocus, astigmatism, spherical aberration)
VI. Fourier Optics (Periodical patterns, FFT spectrum, airy-patterns)
VII. Aberration II (Seidel and Zernike Aberrations, MTF, PSF, Example: Eye)
VIII. Fourier Optics II (Kirchhoff + Fresnel, contrast, example: Hubble-telescope)
IX. Diffractive Optics Applications (Gratings, holography, IOL, CD/DVD/Blu-Ray-Player)
X. Interference (Coherence, OCT)
XI. Filters and Mirrors (Filters, antireflection, polarization, micro mirrors, DLPs)
XII. Laser and Laser Safety (Laser principle, laser types, laser safety aspects)
XIII. Displays (Pico projectors, LCD, LED, OLED, properties of displays)
The module grade is the grade of the oral exam.
total 120 h, hereof 45 h contact hours (30 h lecture, 15 h problem class), and 75 h homework and selfstudies
Solid mathematical background.
E. Hecht: Optics
J.W. Goodmann: Introduction to Fourier optics
K.K. Sharma: Optics - Principles and Applications
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114253 | Optimization and Synthesis of Embedded Systems (ESI) | WT | 3 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student can develop embedded systems. They can specify, synthesize and optimize their own hardware. They learn the hardware description language and are familiar with the special boundary conditions of the design of embedded systems.
The cost-effective and error-free development of embedded systems represents a challenge that should not be underestimated and which is having an ever greater influence on the added value of the overall system. In Europe in particular, the design of embedded systems is playing an increasingly important economic role in many sectors of the economy, such as the automotive industry, so that a number of well-known companies are already involved in the development of embedded systems.
The lecture deals comprehensively with all aspects of the development of embedded systems at hardware, software and system level. This includes diverse areas such as modelling, optimization and synthesis of systems.
90 hours:
30 hours of lectures (15 weeks*2 hours)
30 hours of self study (15 weeks*2 hours)
15 hours of deepening (coding RTL) to grasp the theory with practical
15 hours exam preparation
| Coordinators: |
Prof. Dr.-Ing. Jürgen Beyerer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110809 | Optimization Methods for Machine Learning and Engineering | WT | 5 | Beyerer, Pfrommer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to formulate practical tasks as optimisation problems and solve them using suitable algorithmic methods.
Learning objectives: The students know
- The most important categories of (convex) optimisation problems and their mathematical foundations
- The associated algorithmic solution methods and their runtime complexity
- Techniques for modelling practical tasks as optimisation problems (machine learning, engineering, finance)
- Methods for transforming and approximating optimisation problems for the use of resource-efficient methods
The term optimization refers to techniques for the identification of the best solution in a complex problem setting. Many applications from machine learning and engineering are based on solving an optimization problem. This lecture introduces the major theoretical and algorithmic tools for solving of convex optimization problems. Practical problems for machine learning, engineering and further application domains are used as illustration. The students apply their knowledge to practical optimization problems in tutorial exercises.
Lecture with 2 SWS + 1 SWS exercise
5 ECTS corresponds to approx. 150 hours
approx. 30 hours lecture attendance,
approx. 15 hours attending exercises,
approx. 90 hours of post-processing and working on the exercise sheets
approx. 30 hours exam preparation
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114221 | Parallel Algorithms | WT | 4 | Sanders |
| T-INFO-114222 | Parallel Algorithms Pass | WT | 1 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students acquire a systematic understanding for algorithmic problems and their solutions in the field of parallel algorithms, building on existing knowledge in algorithmics. Additionally, they are able to apply learned techniques to related problems and to interpret and comprehend current research topics.
After successful attendance of the course, the students are able to
• explain terms, structures, basic problem definitions and algorithms from the lecture;
• decide which algorithms and data structures are suitable for solving a given problem and, if necessary, adapt them to the requirements ofa specific problem;
• execute algorithms and data structures, conduct a mathematically precise analysis, and prove their algorithmic properties;
• explain machine models from the lecture and analyze algorithms and data structures in them;
• analyze new problems from application contexts, reduce them to their algorithmic core and design an abstract model; design own solutions in this model using concepts and techniques from the lecture, analyze them and prove the algorithmic properties.
Models and their relation to real machines:
• shared memory - PRAM
• message passing - BSP
• circuits
Analysis: speedup, efficiency, scalability
Basic techniques:
• SPMD
• parallel divide-and-conquer
• collective communication
• load balancing
Concrete algorithms (examples):
• collective communication (including large data volumes): broadcast,
• reduce, prefix sums, all-to-all exchange
• matrix computations
• sorting
• list ranking
• minimum spanning trees
• load balancing: master worker with adaptive problem size, random
• polling, random distribution
Lecture and exercise with 3 semester hours per week, 5 ECTS correspond to approx. 150 working hours, consisting of
• approx. 30 h attendance of the lecture and exercise session / block seminar
• approx. 60 h preparation and follow-up work
• approx. 30 h working on exercise sheets / preparation of seminar presentation
• approx. 30 h exam preparation
See partial achievements (Teilleistung)
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114194 | Parameterized Algorithms | Irreg. | 6 | Bläsius |
| T-INFO-114195 | Parameterized Algorithms - Pass | Irreg. | 0 | Bläsius |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students acquire a systematic understanding of the parameterised approach in the runtime analysis of algorithms, as well as the associated techniques for algorithm design, which build on existing knowledge in theoretical computer science and algorithmics. After successfully completing the course, students will be able to
- reproduce and explain basic algorithmic techniques and analysis techniques in the field of parameterised algorithms,
- execute parameterised algorithms by way of example, analyse them with mathematical precision and prove their properties,
- select which algorithms or algorithmic techniques are suitable for a given parameterised problem,
- analyse unknown problems with regard to their parameterised complexity.
Many problems that arise in practice are NP-hard and therefore generally (presumably) cannot be solved in polynomial time. Nevertheless, these problems can often be solved efficiently because the inputs are "benign". One way to formally capture this benignity of the instances is to consider the parameterised complexity. This involves associating a parameter k with each instance, which represents a measure of the complexity of the input. The aim is then to find an algorithm whose runtime depends only polynomially on the input size n but possibly exponentially on the parameter k. Compared to the rough classification of a problem as polynomially solvable or NP-hard, the parameterised approach offers a much more differentiated view of hard problems.
Lecture with tutorial with 4 SWS, 6 CP
6 CP corresponds to approx. 180 working hours, of which
approx. 60 hours attending the lecture and tutorial
approx. 30 hours of preparation and follow-up work
approx. 60 hours working on the exercise sheets
approx. 30 hours exam preparation
Basic knowledge of algorithms and data structures (e.g. from the lectures Algorithms 1 + 2) is helpful.
| Coordinators: |
Prof. Dr. Kathrin Gerling
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114199 | Participatory Technology Design | WT | 6 | Gerling |
| T-INFO-114200 | Participatory Technology Design - Pass | WT | 0 | Gerling |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
After completing the course, students will be able to reproduce basic and advanced theoretical concepts from human-machine interaction and participatory technology design. Furthermore, they will be able to apply relevant methods for participatory design and evaluation to given problems, taking into account the needs of users and ethical aspects, and derive concrete design recommendations from the results. Finally, students are able to recognise and critically reflect on the connections between participation, design, implementation and evaluation of technologies.
In human-machine interaction, the participatory development of new technologies - i.e. the direct and equal involvement of users in the development process - is becoming increasingly important. It is used, for example, in the development of body-centred and wearable systems, or contributes to the design of solutions in the field of smart and assisted living and personal robotics. Participation is often realised through interviews, focus groups and design workshops; new technologies are also regularly evaluated as part of user studies. The direct involvement of users is intended to ensure that the resulting technologies are better adapted to people's needs and that their benefits and relevance for individuals and society can be increased as a result.
The lecture deals with current research approaches to the participatory design of new technologies and covers the following topics in particular, while continuously taking ethical aspects into account:
- Design approaches, in particular theory-driven design, ethical approaches such as value-sensitive design, and future-oriented approaches such as speculative design and design fiction
- Typical methods of participation in the design and development of technologies, and reflection on the associated challenges and opportunities
- Participatory and user-centred evaluation of technologies and implications for society, research and development
In the associated exercise, students actively develop relevant concepts and reflect on theoretical aspects in their application in the context of practical examples. In addition, current research publications are analysed as part of the exercise.
The total workload for this course is approx. 180 hours (6 credits).
Approximately...
28h for attending the lecture,
24 hours for attending the exercises,
40h for preparation and follow-up of the lecture,
40h for preparation and follow-up of the exercise,
48h for exam preparation.
Knowledge of the basics of human-machine interaction is helpful.
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115210 | Photorealistic Rendering | ST | 5 | Dachsbacher |
See Partial Achievements (Teilleistung)
See Partial Achievements (Teilleistung)
Students understand algorithms and techniques for generating realistic images (e.g., reflection models, light transport simulation, Monte Carlo methods), can analyze and evaluate them, and can select and implement appropriate rendering techniques for a given application.
Algorithms and techniques in computer graphics for generating photorealistic images. Topics include: global illumination and light transport phenomena, path tracing, photon mapping, radiometry, BRDFs, radiosity, Monte Carlo methods, and importance sampling.
60 hours = 1. attendance, time of attendance, face-to-face teaching (Studierende), 2. core working hours (Arbeitnehmer); 3. contact hours (in Modulbeschreibung)
70 hours = preparation/follow-up
20 hours = Exam preparation
See Partial Achievements (Teilleistung)
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114987 | Post-quantum Cryptography from Lattices | Irreg. | 3 | Müller-Quade |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
The student
- knows the assumptions and framework underlying lattice-based cryptography.
- understands and is able to explain the common tools of the area.
- knows and understands the protocols and proofs presented in the lecture and can reproduce them.
- can apply the contents of the lecture to construct new (simple) schemes or analyze the security of given (simple) schemes.
The lecture “Post-Quantum (Lattice-based) Cryptography” introduces the framework of lattice-based cryptography, its common tools and concepts, and important constructions of advanced cryptographic primitives. The central hardness assumptions underlying lattice-based cryptography are believed to be intractable even for quantum algorithms, and as such provide a foundation for post-quantum security. Beyond that, lattice-based cryptography enables a wealth of constructions of theoretical and practical interest.
90 h
Lectures: 24 h
Self-study during semester: 31 h
Preparation for the exam: 35 h
See Partial Achievements (Teilleistung).
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115130 | Practical Course: Advanced Software Development Tools | WT+ST | 6 | Schaefer |
Students will be able to:
· Understand academically driven problems and, in collaboration with advisors, define requirements for the software to be developed or enhanced.
· Create medium-sized programs using current development environments, or familiarize themselves with medium- to large-scale programs and further develop them.
· Present project progress to advisors at regular meetings and identify potential obstacles.
· Evaluate third-party programs during code reviews, identify potential vulnerabilities, and discuss them.
· Present a (further) developed program in a short presentation.
· Document a (further) developed program.
The requirements to modern software are becoming increasingly stringent and complex. As a result, new software development techniques are constantly being introduced to meet these demands. This often requires researchers to implement new development environments and tools that support these advanced development techniques.
In this module, participants will use and expand advanced software development tools from both industry and research. The goal is either to extend the functionality of the tool or to improve its non- functional properties.
Practical work is graded individually, and group work is required. The practical course is integrated into the department’s current research projects and offers ample opportunity for creativity. The assignments are practice-oriented and prepare students for real-world tasks in research and industry.
The module can also be offered in German by arrangement.
20 h familiarization + 120 h development work + 20 h weekly meetings and their follow-up + 10 h preparation and
code review + 10 h preparation and holding of the final presentation = 180 h
| Coordinators: |
Prof. Dr. Achim Streit
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111803 | Practical Course: Advanced Topics in High Performance Computing, Data Management and Analytics | Irreg. | 6 | Streit |
See partial achivements (Teilleitung)
See partial achivements (Teilleitung)
Students know and can apply tools and techniques in the fields of high-performance computing, data management and data analysis. They acquire the possibility to analyze complex scenarios and develop solutions for this. Besides working on the content, students improve their competences in communication and presentation.
Participants will have the chance to deepen their knowledge of high-performance computing, data management and data analysis and to apply it in a practical way. The tasks to be worked on come from the subfields:
• HPC simulations (e.g., parallelization, MPI, performance engineering)
• HPC systems and operating environment (e.g., On Demand File Systems, Infiniband Networks, Job Scheduling)
• Machine Learning and Data Mining (e.g., RapidMiner, scikit)
• Data-Intensive Computing (e.g., Hadoop, Spark).
• HPC and data analysis with Python (e.g., Numpy, Scipy, Pandas, Dask, Parsl)
• Distributed & Parallel File Systems (e.g., glusterFS, BeeGFS)
• Object Storage (e.g., S3, CEPH)
• Data Management System (e.g., dCache, iRods)
• Databases (e.g., SQL, NoSQL)
• Workflow management systems for HPC and data analysis (e.g., FireWorks, AiiDA, SimStack)
• Opportunistic resource integration and utilization (e.g., using COBalD/TARDIS)
• Authentication and authorization infrastructure (e.g., OpenID, SAML)
Students are individually supervised by scientific staff of the Scientific Centre for Computing and can apply their skills in a practical and research-oriented way by being involved in current research tasks (e.g., Helmholtz program, BMBF and EU projects).
3 SWS = 150 h per semester
• 12 h in meetings during the semester (kick-off, regular meetings with the supervisor, final meeting including presentation)
• 18 h preparation of meetings
• 120 h working on the topic and preparation of the exam
Knowledge in the area of databases, data management, data analytics, parallel computing is helpful.
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113659 | Practical Course: AI for Climate and Weather Predictions | Irreg. | 6 | Nowack |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students will be able to
• define current opportunities and challenges in building advanced AI models for climate and weather predictions.
• explain advanced AI model architectures.
• generate and critically assess output of state-of-the-art AI models.
• professionally present their results both orally and in a concise scientific paper.
Students will learn how to work with state-of-the-art AI models for climate science and weather forecasting.
For example, typical AI models will include recent releases of
• Foundation models for climate science and weather forecasting.
• Generative AI models for tasks such as ensemble generation of weather forecasts and of climate change simulations for uncertainty quantification.
• Transformer and graph neural network models for weather forecasting.
• Climate model emulators.
Each student will be able to select from a variety of topics to explore in their practical experiments. These could include, but are not limited to:
• The representation of physical concepts in data-driven AI models (e.g., does the model indirectly learn to “understand physics”?).
• Detecting and understanding failure modes of AI models.
• Forecast accuracy and uncertainty quantification for AI-generated ensembles of simulations.
• Effective solutions to post-processing AI results and/or to modifying AI model architectures.
• Assessing if certain AI architectures perform significantly better for specific tasks.
In-person introductory session, individual and group meetings, final presentation sessions: 30h
Practical tasks – getting started, implementation, experiments, analysis: 100h
Write up results in the style of a scientific paper and preparation of final presentation: 50h
• Knowledge of the Python programming language.
• Good knowledge of mathematical concepts such as linear algebra is an advantage.
• An interest in scientific questions around climate science and weather forecasting.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113958 | Practical Course: Application Security | WT+ST | 4 | Müller-Quade |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification objective:
Students are able to identify security-relevant weaknesses and errors in a program analysis and suggest corrections.
Learning objectives:
This module is dedicated to techniques for exploiting programming errors and common countermeasures, such as:
Attendance time: 15 h
Solving the tasks: 75
Preparation for exam: 30
(1 SWS + 5 SWS) x 15 + 30 h exam preparation = 120 h
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113760 | Practical Course: Artificial Intelligence & Security Lab (AISEC-Lab) | ST | 4 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know how to apply basic concepts of artificial intelligence and machine learning, and are able to evaluate the performance of such systems on real-world data from computer security research.
- Students know and understand concepts of machine learning for computer security.
- Students are able independently design, implement, and evaluate learning-based systems.
- Students understand limits of learning-based approaches.
In this practical course, the students develop learning-based systems for different computer security tasks, thereby intensifying their knowledge gained in the corresponding lectures. The students have the unique opportunity to design, implement, and evaluate systems based on real-world data used in computer security research.
The module is composed of multiple units with several individual tasks/challenges covering different topics from classical computer security research to security of artificial intelligence. In each unit, the students develop an approach, train and validate it on known data, and submit their solution to the course platform, where the approach is tested against unknown data.
- 30h attendance time
- 70h Time to complete the exercises
- 15h Preparation of final presentation
- 5h attendance time (final event)
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114550 | Practical Course: Building PETs | WT | 4 | Schadt, Strufe |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to implement basic methods to handle data in a privacy-preserving manner.
This practical course teaches students the basics of Privacy Enhancing Technology implementations. The methods consist of data privacy notions (k-anonymity and Differential Privacy), private information retrieval and anonymous communication. Students must implement those methods in small example programs in a programming language of their choice.
Participation slots are limited. The sign-up modality can be found on the institute website.
Attendance time: 9h
Practical work: 100h
Lecture preparations/follow-up: 11h
Familiarity with a programming language and a HTTP library
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114298 | Practical Course: Chip Design I | WT+ST | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Hands-on, practical learning by working on hardware-related projects
The Project Lab is a unique opportunity for students to engage in hands-on, practical
learning by working on hardware-related projects from various fields, including
- Hardware-based Neural-Networks implementation
- Neuromorphic computing
- Printed Electronics
- Computation in Memory
- Open-source electronic design automation (EDA) tools extension
- Field Programmable Gate Arrays (FPGA)
- Risc-V architecture
- Hardware Security
- Reliability and Test
- other Emerging Technologies
Students can work individually or in groups of 2-4, collaborating to tackle challenges
based on a selected topic. The lab accepts a limited number of participants each term
based on the number of offered projects, ensuring a focused and immersive
experience for everyone involved.
Project topics are carefully defined and curated each term to align with active research
initiatives within the Chair of Dependable Nano Computing (CDNC). Participants not
only contribute to these projects but also have the potential to co-author research
papers and make tangible contributions to the academic community.
The lab emphasizes practical skills, especially in hardware-related fields, offering
students access to state-of-the-art tools and technologies. It provides an invaluable
opportunity to bridge the gap between theory and practice while preparing for a future
in research, development, or industry.
4 SWS of practical workload including meetings with the supervisor. 90h
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114299 | Practical Course: Chip Design II | WT+ST | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Hands-on, practical learning by working on hardware-related projects
The Project Lab is a unique opportunity for students to engage in hands-on, practical
learning by working on hardware-related projects from various fields, including
- Hardware-based Neural-Networks implementation
- Neuromorphic computing
- Printed Electronics
- Computation in Memory
- Open-source electronic design automation (EDA) tools extension
- Field Programmable Gate Arrays (FPGA)
- Risc-V architecture
- Hardware Security
- Reliability and Test
- other Emerging Technologies
Students can work individually or in groups of 2-4, collaborating to tackle challenges
based on a selected topic. The lab accepts a limited number of participants each term
based on the number of offered projects, ensuring a focused and immersive
experience for everyone involved.
Project topics are carefully defined and curated each term to align with active research
initiatives within the Chair of Dependable Nano Computing (CDNC). Participants not
only contribute to these projects but also have the potential to co-author research
papers and make tangible contributions to the academic community.
The lab emphasizes practical skills, especially in hardware-related fields, offering
students access to state-of-the-art tools and technologies. It provides an invaluable
opportunity to bridge the gap between theory and practice while preparing for a future
in research, development, or industry.
4 SWS of practical workload including meetings with the supervisor. 90h
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115043 | Practical Course: Continuous Software Engineering | WT+ST | 6 | Koziolek |
See Partial Achievements (Teilleistung)
See Partial Achievements (Teilleistung)
Students are able to
· understand scientifically motivated problem statements and, in cooperation with supervisors, derive requirements for the software to be developed or extended.
· develop medium-sized programs using current development environments, or familiarize themselves with medium- to large-scale software systems and further develop them.
· present project progress to supervisors in regular meetings and identify and communicate potential obstacles.
· evaluate third-party code in the context of code reviews, identify potential weaknesses, and discuss them.
· present a (further) developed software system in a short presentation.
· document a (further) developed software system
Agile software development methods refer to a class of iterative and incremental development approaches that place particular emphasis on executable software, early feedback from stakeholders, and embracing necessary changes. In these approaches, efforts that are not directly related to producing running software are viewed critically.
In this lab course, participants use and extend state-of-the-art tools from both practice and research to support agile software development. The following types of tools are considered:
- tools that leverage large language models (LLMs) to support agile software development methods, e.g., creating trace links from existing software engineering artifacts and models
- tools for semi-automatically deriving models from source code and runtime information, in particular performance data
- tools for simpler, domain-specific modeling
- tools for automatically maintaining consistency between development artifacts and models
Depending on the chosen topic, different technologies and programming languages are used, including:
- LLMs
- Java, Python, Java/TypeScript
- Spring Boot, React, Maven, EMF (Eclipse Modeling Framework)
- development of VS Code extensions or language servers
- Vitruvius
- Palladio Simulator
- ARDoCo
- JPlag
Course assessment is graded individually; group work is intended. The lab is embedded in the chair’s ongoing research and offers plenty room for creativity. The assignments are practice-oriented and prepare students for realistic tasks in both research and industry.
The lab can be completed entirely in English. If preferred, supervision and presentations in German are also possible.
In earlier semesters, this course was named „Praktikum: Werkzeuge für agile Modellierung “.
Course workload:
20 hours of work for onboarding,
120 hours of work for development,
20 hours of work for weekly meetings and follow-up,
10 hours for preparing and conducting a code review,
10 hours for preparing and delivering the final presentation.
See Partial Achievements (Teilleistung)
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
(Usage from 7/13/2026)
Elective Studies in Informatics (Usage from 7/13/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115314 | Practical Course: Customized Embedded Processor Design | ST | 4 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student will be able to adapt a processor to specific applications with the help of suitable tools so that it is particularly efficient in terms of performance and power consumption. The student will be able to synthesize and simulate the design.
The design of embedded processors has made rapid progress in recent years. This development has been and continues to be characterized by the increasing demand for application-specific solutions in order to meet the diverse and sometimes contradictory requirements for low power consumption, high performance, low costs and, above all, a fast time-to-market.
This is where the internship comes in. The use of an embedded processor tool suite is practiced. Specifically, application-specific processors are developed for embedded applications, whereby the main focus is on adapting the application-specific instruction set. The description of the customized
processor is then evaluated for functional correctness and efficiency, e.g. performance/power consumption, performance/chip area, etc., on an FPGA platform after various simulation and synthesis steps. If necessary, some or all design steps are iterated several times in order to find an optimal solution. One learning objective is to see that optimizations at a high level of abstraction are particularly effective.
1. attendance time in the internship: 36 h
2. Preparation/follow-up of the same: 24
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
(Usage from 10/1/2026)
Area of Specialization: Telematics (Usage from 10/1/2026) Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115246 | Practical Course: Decentralized Learning Lab | WT | 3 | Hartenstein |
See partial achivements (Teilleitung)
See partial achivements (Teilleitung)
Students know the foundations of Federated and Decentralized Learning. They know and can apply techniques in these fields. They can identify and explain important trade-offs, attacks and defenses in this field. Students are able to independently implement various algorithms and identify fitting solutions for several important application scenarios.
This lab introduces students to the principles and practice of federated and decentralized machine learning. In the first phase, participants implement standard federated learning across a range of application scenarios, gaining hands-on experience with distributed training and aggregation. The course then transitions to decentralized federated learning, removing the central coordinator and exploring peer-to-peer collaboration.
Students will analyze and implement common attack vectors, such as poisoning and inference attacks, and evaluate corresponding defense mechanisms. A common theme throughout the lab is the privacy-robustness-efficiency-trilemma, examining the fundamental trade-offs between protecting user data, ensuring model integrity, and maintaining system performance. By the end, students will be equipped to design and critically assess secure and efficient decentralized learning systems.
In class: 45 h (meetings, reviews, presentation, etc.)
Independent work: 45 h
Sum: 90 h = 3 ECTS
Basic knowledge of Machine Learning and Decentralized Systems is helpful.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
(Usage from 10/1/2026)
Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115341 | Practical Course: Designing and Conducting Experimental Studies | WT+ST | 6 | Beigl |
Planning and conducting a study on a current research topic in the areas of Human–Computer Interaction, Ubiquitous Systems, and Context-Aware Systems.
Upon successful completion of the seminar, students will be able to:
independently search for, identify, analyze, and critically evaluate relevant scientific literature;
derive the framework conditions and parameters for user studies and experiments from the literature;
design, conduct, and evaluate a study addressing a given research question;
present scientific findings in a structured manner and communicate them to a specialist audience in the form of a short presentation;
apply the principles and techniques of scientific writing to prepare a scientific paper describing the planning, execution, and results of the study.
Students are familiar with the German Research Foundation (DFG) Code of Conduct Guidelines for Safeguarding Good Research Practice and successfully apply these guidelines in the preparation of their scientific work.
The practical course focuses on investigating specific research questions through the design and execution of a user study.
Particular emphasis is placed on designing a user study to address a defined research question, followed by conducting the study and analyzing the collected data. Depending on the research question, the workload associated with the individual components may vary.
The lecture part provides both theoretical and practical knowledge on the planning, execution, and evaluation of user studies. It is intended to serve as valuable preparation for the Master's thesis.
Upon consultation, proof of performance may also be provided in German.
Total: 180h
Recommendation: The student should be familiar with the content of the course “Human Computer Interaction” or similar.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115044 | Practical Course: Engineering Approaches to Software Development | WT+ST | 6 | Reussner |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students are able to
- understand scientifically motivated problem statements and, in cooperation with supervisors, derive requirements for the software to be developed or extended.
- develop medium-sized programs using current development environments, or familiarize themselves with medium- to large-scale software systems and further develop them.
- present project progress to supervisors in regular meetings and identify and communicate potential obstacles.
- evaluate third-party code in the context of code reviews, identify potential weaknesses, and discuss them.
- present a (further) developed software system in a short presentation.
The engineering-oriented development of software is an indispensable prerequisite for building large systems. Accordingly, software engineers must systematically analyze the system’s quality already during software design and, where possible, also predict it.
In this practical course, participants use and extend state-of-the-art tools from both practice and research to evaluate and predict the performance of software systems. These tools provide solutions for the following tasks:
- assessing the scalability of software depending on the execution environment
- maintaining consistency across different development artifacts
The development tasks are drawn from the following areas:
- MDSD (Model-Driven Software Development)
- plugin development
- benchmarking
- reverse engineering
The technologies used include:
- Java, Python, Java/TypeScript
- Spring Boot, React, Maven, EMF (Eclipse Modeling Framework)
- Development of VS Code extensions or language servers
- Vitruvius
- Palladio Simulator
Course assessment is graded individually; group work is intended. The lab is embedded in the chair’s ongoing research and offers plenty room for creativity. The assignments are practice-oriented and prepare students for realistic tasks in both research and industry.
The lab can be completed entirely in English. If preferred, supervision and presentations in German are also possible.
In earlier semesters, this course was named „Praktikum Ingenieursmäßige Software-Entwicklung"
Course workload:
20 hours of work for onboarding,
120 hours of work for development,
20 hours of work for weekly meetings and follow-up,
10 hours for preparing and conducting a code review,
10 hours for preparing and delivering the final presentation.
In total, this amounts to 180 hours of work.
See Partial Achievements (Teilleistung).
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113635 | Practical Course: Fine-grained Algorithm Design and Engineering | Irreg. | 6 | Künnemann-Goos |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students should be able to apply knowledge in the specializations “Algorithmtechnik” and “Theoretische Grundlagen” to derive fast algorithms and their implementations for a given algorithmic problem.
This includes:
– modeling a given problem of interest as a well-defined algorithmic problem as well as identifying reasonable relaxations
– performing a literature search to identify algorithmic ideas previously proposed for a given problem
– researching a given algorithmic or conditional lower bound technique and investigating its applicability on a given problem
– implementing resulting algorithms efficiently
– creating reasonable benchmark data sets (generated randomly, via reductions or from real-world data sources)
– evaluating an implementation on benchmark data and gaining insights on possible improvements of the model, algorithm or implementation.
Furthermore, the students can constructively engage in a team setting and are able to clearly communicate their ideas and results.
Each group of students will receive a topic among a list of possible algorithmic problems with relevance for fine-grained and parameterized complexity (usually from the fields of graph theory, computational geometry or string problems). In some cases, the proposed topic is the subject of an ongoing algorithmic contest (e.g., the PACE challenge), providing an opportunity of participation as part of the practical course.
Under supervision, each group will:
– research the theoretical state-of-the-art for their algorithmic problem and/or design a novel algorithm,
– implement one or more algorithmic approaches
– evaluate and improve them using appropriate benchmark data sets.
The course aims to investigate the connections between worst-case upper & conditional lower bounds and fast practical implementations.
6 CP correspond to ~ 180 h, distributed roughly as follows:
~ 40 h meetings, literature review, etc.
~ 100 h implementation and evaluation
~ 40 h preparation of presentation and report
- Basic knowledge of algorithms and data structures is assumed.
- Knowledge of fine-grained complexity is helpful, but not required.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-105576 | Practical Course: FPGA Programming | WT+ST | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students will learn to design and to simulate digital circuits with FPGA.
This lab emphasizes on the practical aspects of Field Programmable Gate Arrays (FPGAs). In the beginning, a short background introduction on FPGAs is given, followed by a tutorial on the workflow of configuring and programming an FPGA. This lab includes FPGA design using schematic layouts as well as several example of VHDL/Verilog programming to implement some sample digital circuits. Students will learn to design and to simulate digital circuits with FPGA. The design will be compiled on run a FPGA. The lab is designed around the DE2-115 prototyping board, which provides a programmer, program memory, and array of switches, buttons, LEDs, an LCD, and several I/O ports.
4 SWS / 3 CP = 90 h/week
Knowledge of “Dependable Computing” and “Fault Tolerant Computing” and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-109914 | Practical Course: General-Purpose Computation on Graphics Processing Units | WT+ST | 3 | Dachsbacher |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students should acquire the ability to use programmable graphics hardware using suitable interfaces (e.g. OpenCL, CUDA) to solve scientific and technical calculations. The students should thereby acquire the practical ability to systematically develop a parallel, efficient programme on the basis of suitable algorithms. Students learn basic algorithms for parallel architectures, are able to analyse and evaluate them, and practice their use in practical applications.
The practical course covers basic concepts for the use of modern graphics hardware for technical and scientific calculations and simulations. Starting with basic algorithms, e.g. parallel reduction or matrix multiplication, the practical course imparts knowledge about the properties and capabilities of modern graphics processors (GPUs). As part of the practical course, students work on smaller sub-projects in which they acquire knowledge about the algorithms used and apply them to a specific problem; OpenCL or CUDA, for example, is used as a programming interface.
Attendance time = 12h
Preparation/post-processing = 78h
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 10/1/2025)
Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2025) Elective Studies in Informatics (Usage from 10/1/2025) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113393 | Practical Course: Human-Centred Robotics | WT+ST | 6 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to understand and scrutinize complex scientific topics and to reproduce and verify published results. They gain in-depth knowledge and practical experience in the field of motion generation and control of human-centered robots while working on a specific project task. They also learn how to plan, work together and communicate in a team. Students will be able to present their project results in a scientific presentation, demonstrate the practical results and answer detailed questions. They can also summarize their project results in writing using Latex in the style of a scientific paper and place them in a scientific context.
Human-centered robots are robots that directly interact with humans or support humans in their motions. This includes humanoid robots, but also wearable robots (exoskeletons and prostheses) or external physical assistive devices. In this practical course, students learn how to implement theoretical knowledge about human-centered robots and use it to solve a given task based on a individual project with robot hardware. Projects can either focus on code development for a given hardware or on the development or modification of robot hardware along with the basic code. Students learn about the challenges of working with real robot hardware vs model computations, and about working principles and practical implementation of sensors and actuators.
For capacity reasons (supervision of practical projects and workspace) , the practical course is limited in terms of projects and number of students. Specific project topics will be different each term and will be announced in a presentation during the first semester week.
Subsequently, a selection and topic assignment will be carried out, taking into account the students' subject and topic specific skills.
Estimated effort for this module is 180 hours:
20h – In person events (kickoff meeting, individual meetings with supervisor, presentations)
130h – Individual project work
30h - Writing report and preparing presentation
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) are very helpful.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114691 | Practical Course: Interactive Theorem Proving | Irreg. | 3 | Beckert, Stübinger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to use an interactive theorem prover such as Isabelle/HOL
and can formulate and prove formal statements in the field of programming
language technology (e.g. related to type safety, compilers, and semantics).
Students can name and describe the foundational features of the theorem prover, including its meta-logic, unification, resolution and rule application as well as the axiomatisations of higher-order logic.
Furthermore, students are able to write proofs themselves in the proof language. They can assess which proof methods (simplification, case distinction, induction, etc.) are appropriate for a given statement. They are also able to formulate statements which fit a given proof method. Students can apply advanced methods for structuring proofs (e.g. using Isar). They know the automatic and manual tactics of the theorem prover and can decide in which situations these should be applied.
Students are proficient in defining (recursive) data types, functions and inductive predicates and can prove statements about them. Students can implement simple algorithms from the field of program analysis (e.g. constant propagation) and verify its correctness with the help of the theorem prover.
In this lab course students are familiarized with the use of the
interactive theorem prover Isabelle/HOL and use it to formalise and verify a project. In the first half of the course, important principles of theorem proving (e.g. simplification, recursion, inductive definitions and proofs) are learned and practiced via exercise sheets. In the second half of the course, students will work independently in teams formalising and verifying a topic in the area of formal methods, e.g. in programming language theory, type systems, or compilers. Project topics are set at the end of the first half of the course.
3 CP equate to approximately 90 SWS. These are distributed as follows:
· approx. 15 h attendance time
· approx. 30 h work on the exercise sheets
· approx. 43 h work on the independent project
· approx. 2 h work on preparing the project presentation
- Knowledge of functional programming is assumed.
- Some knowledge of logic (e.g. via courses such as Formale Systeme and/or Constructive Logic) will be helpful, but is not required
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-107493 | Practical Course: Internet of Things (IoT) | WT+ST | 4 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- Students will understand the core concepts of IoT systems, including design objectives, application domains, and requirements.
- They will gain skills in developing software programs for IoT embedded devices, debugging, and testing software on hardware.
- They will be capable of integrating and evaluating IoT systems comprising sensors, processors, wireless communication modules, and data storage.
Attendance time: 45 hours
Final project: 55 hours
Final presentation & Report: 20 hours
Total = 120 hours = 4 ECTS
- Familiarity with other (than C) languages like Python could be helpful as well.
- Basic knowledge from the modules “Design and Architectures of Embedded Systems (ESII)” and “Optimization and Synthesis of Embedded Systems (ESI)” are helpful but not essential for understanding the lab.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-108323 | Practical Course: Low Power Design and Embedded Systems | ST | 4 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Upon completion of this lab, students will:
• Apply and evaluate different hardware/software optimization techniques for low power and energy consumption under given constraints (e.g., performance) on embedded systems.
• Develop a deep understanding of system-level resource management techniques in modern systems. They will learn to apply machine learning methods to automate and optimize complex resource allocation decisions, thereby acquiring practical skills in data collection, model training, and iterative system tuning.
• Collaborate effectively in a team to practically solve technical problems related to power, temperature and energy optimizations on a real hardware platform.
This lab explores different software and hardware approaches for power reduction on modern embedded systems, considering other relevant metrics and constraints such as performance, power, temperature, chip area, among others, both on simulation and real-hardware platforms.
The course is divided in two main topics:
• Smart resource management (RM) for multi/-many-core computing systems: This topic covers system level techniques based on RM control knobs such as application mapping, dynamic task migration and dynamic voltage and frequency scaling (DVFS), using both from the heuristic and machine-learning methods.
• Hardware/Software design for low power systems: This topic includes the application of several design principles among the computing stack to optimize different metrics such as performance, power and energy in embedded systems. Techniques include compiler optimizations, HW/SW co-simulation, design-space exploration and high-level synthesis.
Attendance time:
40 hours
Project work:
60 hours
Final Report preparation:
20 hours
Total: 120 hours (4 ECTS)
Students should be familiar with software development practices under Linux-based systems. Practical knowledge in C/C++ as well as Python is required.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2026)
Area of Specialization: Robotics and Automation (Usage from 10/1/2026) Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115259 | Practical Course: Machine Learning and Intelligent Systems | WT+ST | 8 | Hanebeck |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students are able to:
This course offers the opportunity to work on research-related topics in the areas of extended reality, robotics, state estimation, and measurement and control systems. The specific tasks in each semester are based on current research work at ISAS and are published on the lab’s website. The tasks are worked on by small groups of up to three students, by designing, implementing, and testing suitable hardware and/or software components. Each group is supported by a designated supervisor regularly giving personal feedback and advice. The main goal of the course is the implementation of theoretical methods in real-world applications. An initial familiarization and brainstorming phase is followed by an extensive practical phase in which, in addition to the implementation work, project and time management skills are trained. In parallel, results are documented in the form of presentations and a final report.
http://isas.iar.kit.edu
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 10/1/2025)
Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2025) Elective Studies in Informatics (Usage from 10/1/2025) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114911 | Practical Course: Mathematical and Computational Methods in Robotics and AI | WT+ST | 6 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to understand scientific topics. They will learn how to address specific project tasks and solve them in a structured way. They will obtain deepened knowledge about fundamental mathematical and computational methods, including their potential, strengths and limitations and in which areas they are suited to be applied. Students learn to present their results to a an audience, as well as suitably documenting their results in form or in addition to a scientific paper and being capable of assessing the advantages and disadvantages of the methods used.
Efficient mathematical and computational methods are an irreplaceable cornerstone of modern research and industrial applications in forms such as numerical simulation, optimization or inference, ranging from the generation of task-specific trajectories for robotic systems over stabilization of existing robots to data-driven prediction and inference as well as AI-applications.
In this practical course, students learn, on the basis of an individual programming project, how to implement and apply fundamental mathematical and computational methods to solve an existing task in a robotics application or related fields and gain experience leveraging methods from this area in said real-world context.
Limited number of projects and participants. Specific project topics will be different each term and will be announced in a presentation during the first semester week or can be indivdually agreed upon with people supervising the practical. Students can propose topics on their own as well.
Estimated effort for this module is 180 hours:
20h – In person events (kickoff meeting, individual meetings with supervisor, presentations)
130h – Individual project work
30h - Writing report and preparing presentation
Basic mathematical knowledge will be helpful, depending on topic. For Projects in the context of robotics, knowledge of robotics is recommended, but not strictly necessary.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113897 | Practical Course: Model-Driven Software Development | ST | 6 | Burger, Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can
Model-driven development methods have become particularly popular thanks to the Eclipse Modelling Framework (EMF) and the OMG standards MOF, UML and QVT. Advanced software development concepts such as product lines, generative programming and model transformations now make it possible to develop software more flexibly and quickly and to use it on different platforms. Domain-specific languages (DSL) and the graphical and textual editors generated from them can be easily created.
This practical course deals with current techniques of model-driven software development (MDSD). Students work with current frameworks and languages such as EMF, QVT, ATL and XText and create a domain-specific language and model transformations.
96 working hours for exercises, 48 working hours for project work, 16 working hours for preparing the final presentation, 20 working hours for weekly meetings and final presentation. This results in a total of 180 working hours.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113394 | Practical Course: Movement and Technology | ST | 6 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to analyze and understand complex scientific topics in the area of human motion capture and motion analysis. They gain in-depth knowledge and practical experience with motion capture technology, experiment planning, and analysis. They also learn how to plan, work together and communicate in an interdisciplinary team. Students will be able to present their project results in a scientific presentation, demonstrate the practical results and answer detailed questions. They can also summarize their project results in writing using Latex and place them in a scientific context.
In this joint course between Informatics and Sports Science, and in the sense of research-oriented teaching, students learn about current research projects of the BioRobotics Lab (Informatics) and the BioMotion Center (Sports Science) at the interface of motor control and biomechanics of human movement. This research involves the use of latest motion capture technology, advanced analysis tools, and partly also assistive robotics technology. Students work in in teams (interdisciplinary teams between students from different study programs are highly encouraged) to carry out motion capture experiments, analyze the data and present the results in written and oral form. Depending on the specific project, these motion capture studies are either stand-alone studies just for this course or part of a larger research project at one of the organizing research groups.
Limited number of projects and participants. Specific project topics will be different each term and will be announced in a presentation during the first semester week.
Estimated effort for this module is 180 hours:
20h – In person events (kickoff meeting, individual meetings with supervisor, presentations)
120h – Individual project work
40h - Writing report and preparing presentation
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) are very helpful.
Programming skills.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114206 | Practical Course: Natural Language Dialog Systems | Irreg. | 6 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student
Thanks to major advances in the field of deep learning and, in particular, large language models, it is now possible to develop dialogue systems and chatbots that can support people in many situations.
As part of this internship, students will develop a personal assistant for various application scenarios. To do this, students must first deal with data collection and data preparation. This data should then be used to develop a chatbot for the addressed application using freely available pre-trained models. In addition, the students will investigate various options for evaluating the systems.
In the final part of the internship, students can independently choose a focus to improve their initial system. The final systems will be presented in a final presentation.
180h
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
(Usage from 4/1/2026)
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113122 | Practical Course: Neural Network Acceleration on FPGAs | WT+ST | 3 | Gnad, Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
After the lab is finished, the students will have acquired the following skills and expertise:
- Be able to implement a neural network accelerator based on established benchmark data, generating an ONNX model and finishing with a dedicated FPGA design, based on the open source FINN framework for AMD FPGAs.
- Understand the implications of using quantization and other resource-constraining methods.
- Be able to understand the distinctive advantages (or disadvantages) of FPGAs versus other implementations.
Neural networks are applied in a variety of domains, even critical application scenarios in transportation and medicine. Important aspects of accelerating neural networks in various application domains are performance, latency, reliability, and energy footprint. Dedicated hardware can have advantages in all of these domains over a traditional CPU and also GPU implementations. In this regard, Field-Programmable Gate Arrays (FPGAs; reconfigurable hardware) have shown to be an efficient and versatile solution for accelerating quantized neural networks, which are compact representations of neural network models. Their benefits are proven by the use in Microsoft Azure ML, Amazon AWS and other cloud platforms.
This module will teach students how to implement neural networks on reconfigurable hardware using an established framework, and also looks into relevant practical details when optimizing the network for hardware deployment.
(2 SWS + 2 x 2 SWS) x 15 = 90h = 3 ECTS
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
(Usage from 10/1/2026)
Area of Specialization: Software Engineering and Compiler Construction (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115280 | Practical Course: Performance Engineering in C++ and Rust | Irreg. | 6 | Groot Koerkamp, Maas |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students
- can use the methods of algorithm engineering in order to
implement and evaluate given algorithmic problems and data structures in
C++ or Rust.
- recognize factors that lead to inefficient code and can, if possible, replace them with more efficient constructions.
- understand how the presented techniques can be used to optimize
CPU-bound code using SIMD instructions and multi-threading and to optimize memory-bound code using cache-efficient data structures.
- know the features of the standard library and are able to use them selectively.
- can test, profile, and benchmark the generated code for correctness and performance, furthermore they can represent and analyze the obtained results.
In this practical course students solve multiple programming tasks in C++ or Rust. The main focus is on the efficient implementation and
their evaluation through extensive experiments. The programming tasks are motivated by scientific work in the field of algorithm
engineering.
They cover complex algorithms as well as advanced data structures, furthermore
advanced programming techniques are used, such as SIMD, multi-threading, and prefetching.
This course used to be called "Efficient Parallel C++".
~ 15h attendance time
~ 25h presentation/discussion/assessment of the solutions (with preparation)
~ 140h working on the tasks (implementation and evaluation)
At least basic knowledge of the C++ language *or* Rust language is necessary for
participation in the course. Students should be able to implement given
algorithms.
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115486 | Practical Course: Programming Language Design and Implementation | Irreg. | 6 | Erdweg |
See Partial Achievements (Teilleistung)
See Partial Achievements (Teilleistung)
Upon completion of this practical course, students will be able to:
· Name and describe the different parts of a language implementation.
· Distinguish and compare alternative implementation techniques for programming languages.
Contribute to and adapt parts of a language implementation.
In the first few weeks of this course, students are introduced to the different parts and techniques for implementing programming languages. This may include for example: language design, frontend, type checking and inference, static analysis, macros and metaprogramming, code generation, optimization, run-time systems, memory management, and debugging.
After the introductory sessions, students will work in independent groups to develop a new language implementation or extend an existing one. Project topics will be set after the introductory part of the course.
6 CP equate to approximately 180 SWS. These are distributed as follows:
· approx. 30 h in-person meetings (introductory lectures, individual meetings with project supervisors, presentations)
· approx. 40 h self-study of existing language implementations
· approx. 100 h work on the independent project and its presentation
· approx. 10 h write a project summary
Very good programming skills.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113350 | Practical Course: Real-world Vulnerability Discovery and Exploits | WT+ST | 4 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
← Students know and understand exploitation techniques.
← Students are able to independently research software vulnerabilities.
← Students are comfortable engaging with software vendors in vulnerability disclosure.
Students understand modern exploitation techniques and can apply them. Furthermore, they get familiar with the vulnerability disclosure process of prominent software vendors, reporting their findings.
- 2h attendance time/ week (lectures)
- 5h project work/ week
- 10h preparation for final presentation
- 5h attendance time (final event)
Total 120h
Application security internship
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114899 | Practical Course: Route Planning | Irreg. | 6 | Bläsius, Ueckerdt |
See partial achievements. (Teilleistung)
See partial achievements. (Teilleistung)
The students
- can practically apply the knowledge from the the module "Algorithmen für Routenplanung",
- are capable of efficiently implementing given topics in route planning (e.g., separator computation, shortest path acceleration techniques, ...),
- master the steps from design to implementation and evaluation in the practical application of algorithmic methods,
- possess the ability to act in a results-oriented manner within a team, critically assess their own actions, and demonstrate strong communication skills.
The participants are furthermore able to efficiently implement algorithms with consideration for modern computer architectures, as well as to plan and conduct meaningful experimental evaluations.
In the practical course route planning, various topics from route planning are assigned, which are to be implemented independently by small student groups.
A main focus is placed on efficient programming in C++ or Rust.
Practical course with 4 SWS, 6 LP
6 LP corresponds to approx. 180 working hours, of which
approx. 10 hours attendance time,
approx. 12 hours working on the exercises,
approx. 128 hours implementation phase,
approx. 30 hours elaboration and preparation of the presentation
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110990 | Practical Course: Security, Usability and Society | Irreg. | 4 | Strufe |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know established security and data protection programs, can implement them in apps and can carry out user studies.
Learning objectives:
- Students know and understand the methods for developing privacy-friendly apps and can apply them.
- Students are able to implement various applicable security measures in programs.
- Students can set up and conduct user studies.
- Students are able to prepare and present a report of their work.
The internship "Security, Usability and Society" covers topics such as usable security and privacy programs as well as conducting user studies.
Topics include:
- Privacy-friendly apps
- Programming usable security measures
- Conducting usable security user studies
Attendance time: 15 h
Solving the tasks: 75
Preparation of presentation and report: 30
| Coordinators: |
Prof. Dr. Veit Hagenmeyer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112030 | Practical Course: Smart Energy System | WT+ST | 6 | Jumar |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
After successfully completing the course, students will be able to
- explain and categorize a set of current research issues in modern energy systems
- explain structure and objectives of the KIT Energy Lab and its role in smart grid research
- conduct selected research activities in the field of innovative, application-oriented information, automation and system technology for sustainable energy systems
- isolate a research activity / hypothesis suitable for evaluation in the Smart Energy System Simulation and Control Center
- plan, organize and manage their laboratory / experimental work to fit a given time-frame
- concisely present and defend their research activity and results in front of a peer audience
- structure and writeup their work in research-paper-style
- work in team
As part of the two-week lab course, students work in teams of two on a project topic derived from current research questions of the Smart Energy System Simulation and Control Centre of the Energy Lab (https://www.iai.kit.edu/RPE.php). A list of topics is made available in advance via the Campus Plus Portal. Interested students should indicate their preferences based on which the topics will be assigned.
The group of students typically goes through the following phases: Concept development/experiment
preparation, implementation/experiment execution, evaluation/experiment analysis, presentation of results and write-up. The teams will work independently on the assigned project - supervision is provided by research assistants.
The course begins with a joint kick-off event. This includes an organizational introduction, a tour of the Energy
Lab and a brief presentation of all project topics. For the following preparation phase students are provided with literature and material to thoroughly familiarize themselves with the topic, research background and literature, plan and prepare experiments etc. In the two lab weeks the experiments are conducted and a scientific writing workshop is held. At the end of the lab course each team has to defend their work in a 10 min presentation followed by 5 minutes of discussion in front of peers and examiners. Additionally, the work needs to be documented in the style of a scientific paper.
6 credit points corresponds to approx. 180 working hours, of which
- Attendance time / meetings in large and small groups: 10h
- Select and carry out project work: 140h
- Writing a research report and preparing a presentation: 30 hours
- Knowledge of the fundamentals of energy informatics is a prerequisite.
- Knowledge of the fundamentals of electrical engineering and energy technology is required.
- Knowledge of the basics of mechatronics, data analysis and signal processing is helpful.
- Knowledge of power systems or power electronics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-113854 | Practical Course: Software Development and Application of Mobile, Bio-Inspired Robots | WT | 6 | Rönnau |
See partial achievement
None
Students are able to understand biologically inspired mobile robots and extend their software.
This module teaches students how to work with and expand biologically inspired mobile robotics. The topics covered include control engineering, computer vision, 3D mapping, navigation and human-machine interaction.
Students work in groups and produce a joint final report and presentation.
180h
- 30h weekly regular meeting
- 120h preparation and follow-up time
- 30h Presentation and report preparation
See partial achievement
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-103000 | Practical Course: Visual Computing | WT+ST | 6 | Dachsbacher |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
In this course, practical problems from the core area of computer graphics and the broader field of visual computing are solved where graphics hardware is used. In individual sub-projects, or self-defined larger projects, the application of various computer graphics techniques and the use of modern graphics hardware are practised. In addition, students can work together in a team to solve the tasks of the work placement.
The practical course deals with specific topics, some of which were addressed in corresponding lectures on the specialisation subject of computer graphics, and explores these in greater depth. Previous attendance of the respective lecture is helpful, but not a prerequisite for attendance.
Attendance time = 30h
Preparation/follow-up = 150h
Programming skills in C/C++ are recommended.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Design of Embedded Systems and Computer Architectures Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114267 | Practical Introduction to Hardware Security | WT+ST | 6 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The goal of this course, which is a combination of lectures and lab assignments, is to have a hands-on experience on basic concepts and new developments in hardware security, by combining both theory and practice in a coherent course. The theoretical concepts for each topic will be presented to the students in form of lectures, followed by a set of lab assignments on both hardware and software platforms to be performed by the students for each topic.
1. Hardware security primitives (PUF, TRNG)
2. Hardware Implementation of encryption modules (AES)
3. Passive Attack with side channel (on AES)
4. Active fault attack (on AES)
4 SWS / 6 ECTS = 180h
2 SWS lecture (1,5h) + 2 SWS practical course (1,5h) / week
Knowledge of Digital Design (lecture TI)
Practical Course “FPGA Programming”
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114262 | Practical SAT Solving | Irreg. | 5 | Balyo, Iser, Sanders, Schreiber |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to evaluate combinatorial problems, assess their complexity, and solve them using computers.
Students learn how to solve combinatorial problems efficiently using SAT Solving. Students are able to assess the practical complexity of decision and optimization problems, encode problems as SAT problems, and implement efficient solution procedures for combinatorial problems.
Students gain insight into state-of-the-art solution methods for SAT and related problems and their implementations in SAT solvers.
The problem of propositional satisfiability (SAT) is an outstanding problem of computer science from a theoretical as well as practical perspective. Being the first problem proven to be NP-complete, it serves as a fundamental tool for research in complexity theory. Moreover, SAT solving has been established as one of the most important fundamental methods in hardware and software verification, and is used to solve hard combinatorial problems in industrial practice as well. This module aims to provide students with the theoretical and practical aspects of SAT-Solving. Covered are:
1. basics, historical development
2. encodings, e.g. cardinality constraints
3. phase transitions in random problems
4. local search (GSAT, WalkSAT, ..., ProbSAT)
5. resolution, Davis-Putnam algorithm, DPLL algorithm, look-ahead algorithm
6. efficient implementations, data structures
7. heuristics in the DPLL algorithm
8. CDCL algorithm, clause learning, implication graphs
9. restarts and heuristics in the CDCL algorithm
10. preprocessing, inprocessing
11. generation of proofs and their checking
12. parallel SAT solving (guiding paths, portfolios, cube-and-conquer)
13. related problems: MaxSAT, MUS, #SAT, QBF
14. advanced applications: Bounded model checking, planning, satisfiability-modulo-theories
Lecture (2 SWS) + exercise (1 SWS)
(Preparation and follow-up: 4h/week, exercises: 2h/week, preparation for exam: 15h)
Total workload: (2 SWS + 1 SWS + 4 SWS + 2 SWS) x 15 h + 15h preparation = 9x15h + 15h = 150h = 5 ECTS
| Coordinators: |
Prof. Dr. Bernhard Beckert
Prof. Dr.-Ing. Michael Beigl
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Interdisciplinary Qualifications
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115231 | Practice of Research (Methods, 1st Semester) | WT+ST | 2 | Beckert |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
The aim of “Practice of Research” is to acquire both subject-specific knowledge and methodological skills for scientific work, and to apply them to an actual research project.
After completing all four modules of “Practice of Research,” participants will be able to:
• conduct a literature search based on a given topic, independently identify, locate, classify, evaluate, and analyze relevant literature,
• present the results of the literature search to a specialist audience in their own words using self-created presentation slides, and discuss them critically,
• formulate, define, and articulate the relevance of a research question or research problem,
• explain the basics of the philosophy of science and relate them to their project,
• discuss the fundamentals of the research approach used (e.g., experimental design and execution) and apply them to their project,
• design, justify, evaluate, and position their own research (sub-)approach,
• derive concrete work steps and a project plan from the research question and the chosen approach,
• determine workloads, coordinate tasks, and, if applicable, allocate them within a team,
• identify and analyze risk factors and develop and plan countermeasures,
• conduct scientific work in the project’s research field,
• identify, plan, and carry out the necessary preparatory work for the project,
• work scientifically in the project’s research area,
• know and apply the relevant substantive foundations for the project and assess their relevance to the research question,
• document, summarize, and present their planning and project progress,
• recognize and evaluate progress and develop and apply steering measures,
• name, explain, and apply techniques of scientific work and scientific writing,
• plan, prepare, and evaluate scientific publications,
• document, summarize, and illustrate the project process and results,
• work scientifically in collaboration with others or in a team.
The content of “Practice of Research” is the guided execution of a scientific research project. Over a total period of two semesters, intensive and continuous work on the project takes place. Within the framework established by “Practice of Research,” students acquire both subject knowledge and methodological competence for scientific work.
The topics of the participants’ projects come from the research areas of the respective supervisors. In general, the project takes place within an ongoing research initiative, which ensures a strong integration of research and teaching.
The emphasis in the first semester is on project planning and carrying out preparatory work. The emphasis in the second semester is on project execution and presentation of results.
At the end of “Practice of Research” (end of the second semester), participants write a scientific paper on the project results. This work should meet the quality standards of a scientific publication and, if possible, be published.
Participation in “Practice of Research” also serves as preparation for a Master’s thesis for which the scholarly level may go beyond the usually expected scope.
In parallel to the project work, there are accompanying courses in which competencies for scientific and project-oriented work are taught (these are credited as cross-disciplinary qualifications; see the modules “Practice of Research (Methods, 1st semester)” and “Practice of Research (Methods, 2nd Semester)”). The topics of these accompanying courses are:
• scientific research methods;
• methodical search for related works on a research topic, in particular literature search, basic understanding of scientific literature;
• presentation of scientific works;
• teamwork in scientific contexts;
• methodical creation of work plans for scientific projects;
• methodical evaluation of scientific works;
• strategies for conducting scientific projects;
• creating scientific publications.
This module forms a unit with the modules “Practice of Research (Project, 1st Semester)”, “Practice of Research (Project, 2nd Semester)”, and “Practice of Research (Methods, 2nd Semester)”. Across the four modules, a continuous research project is conducted over two semesters.
The module can also be provided in German if needed.
The total workload for this module is 60 hours (the total workload for all four modules of “Practice of Research” is 720 hours).
Teaching corresponding to 0.5 of the module’s 2 credit points is provided by lecturers of the KIT Faculty of Informatics, and teaching corresponding to 1.5 credit points is provided by the House of Competence. The courses offered by the House of Competence are:
- Workshop “Presenting for Computer Science Students (Master)”, 1 CP
- Workshop “Project Management for Computer Science Students (Master)”, 0.5 CP
* Contact hours in workshops and courses: 26 hours
* Preparation and follow-up of the courses: 8 hours
* Reflection and transfer to the context of the student’s own project: 14 hours
* Working on exercise tasks: 4 hours
* Exam preparation: 8 hours
Total: 60 hours (= 2 credit points)
| Coordinators: |
Prof. Dr. Bernhard Beckert
Prof. Dr.-Ing. Michael Beigl
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Interdisciplinary Qualifications
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115232 | Practice of Research (Methods, 2nd Semester) | WT+ST | 2 | Beckert |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
The aim of “Practice of Research" is to acquire both subject-specific knowledge and methodological competences for scientific work, and to test them on an actual research project.
After completing all four modules of “Practice of Research,” participants can:
• conduct a literature search starting from a given topic, independently identify, locate, classify, evaluate and analyze relevant literature,
• present the results of the literature search to a specialist audience in their own words using self-created presentation slides, and discuss them critically,
• formulate, delimit and articulate the relevance of a research question or research problem,
• explain the basics of the philosophy of science and relate them to their project,
• discuss the fundamentals of the employed research approach (e.g., experimental design and execution) and apply them to their project,
• design, justify, evaluate and place their own research (sub-)approach,
• derive concrete work steps and a project plan from the research question and the chosen approach,
• determine workloads, coordinate tasks and, if applicable, allocate them within a team,
• identify and analyze risk factors and develop and plan countermeasures,
• conduct scientific work in the project’s research field,
• identify, plan and carry out the necessary preparatory work for the project,
• work scientifically in the project’s research area,
• know, apply the relevant substantive foundations for the project and assess their relevance to the research question,
• document, summarize and present their planning and project progress,
• recognize and evaluate progress and develop and apply steering measures,
• name, explain and apply techniques of scientific work and scientific writing,
• plan, prepare and evaluate scientific publications,
• document, summarize and illustrate the project process and results,
• work scientifically in collaboration with others or in a team.
The content of “Practice of Research” is the guided execution of a scientific research project. Over a total period of two semesters, intensive and continuous work on the project takes place. Within the framework established by “Practice of Research,” students acquire both subject knowledge and methodological competence for scientific work.
The topics of the participants’ projects come from the research areas of the respective supervisors. In general, the project takes place within an ongoing research initiative, which ensures a strong integration of research and teaching.
The emphasis in the first semester is on project planning and carrying out preparatory work. The emphasis in the second semester is on project execution and presentation of results.
At the end of “Practice of Research” (end of the second semester), participants write a scientific paper on the project results. This work should meet the quality standards of a scientific publication and, if possible, be published.
Participation in “Practice of Research” also serves as preparation for a Master’s thesis for which the scholarly level may go beyond the usually expected scope.
In parallel to the project work, there are accompanying courses in which competencies for scientific and project-oriented work are taught (these are credited as cross-disciplinary qualifications; see the modules “Practice of Research (Methods, 1st semester)” and “Practice of Research (Methods, 2nd Semester)”). The topics of these accompanying courses are:
• scientific research methods;
• methodical search for related works on a research topic, in particular literature search, basic understanding of scientific literature;
• presentation of scientific works;
• teamwork in scientific contexts;
• methodical creation of work plans for scientific projects;
• methodical evaluation of scientific works;
• strategies for conducting scientific projects;
• creating scientific publications.
This module forms a unit with the modules “Practice of Research (Project, 1st Semester)”, “Practice of Research (Project, 2nd Semester)”, and “Practice of Research (Methods, 1st Semester)”. Across the four modules, a continuous research project is conducted over two semesters.
The module can also be provided in German if needed.
The total workload for this module is 60 hours (the total workload for all four modules of “Practice of Research” is 720 hours).
Teaching corresponding to 0.5 of the module’s 2 credit points is provided by lecturers of the KIT Faculty of Informatics, and teaching corresponding to 1.5 credit points is provided by the House of Competence. The courses offered by the House of Competence are:
- Workshop “Presenting for Computer Science Students (Master)”, 1 CP
- Workshop “Project Management for Computer Science Students (Master)”, 0.5 CP
* Contact hours in workshops and courses: 26 hours
* Preparation and follow-up of the courses: 8 hours
* Reflection and transfer to the context of the student’s own project: 14 hours
* Working on exercise tasks: 4 hours
* Exam preparation: 8 hours
Total: 60 hours (= 2 credit points)
| Coordinators: |
Prof. Dr. Bernhard Beckert
Prof. Dr.-Ing. Michael Beigl
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2026)
Area of Specialization: Algorithm Engineering (Usage from 10/1/2026) Area of Specialization: Cryptography and Security (Usage from 10/1/2026) Area of Specialization: Design of Embedded Systems and Computer Architectures (Usage from 10/1/2026) Area of Specialization: Telematics (Usage from 10/1/2026) Area of Specialization: Robotics and Automation (Usage from 10/1/2026) Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Area of Specialization: Software Engineering and Compiler Construction (Usage from 10/1/2026) Area of Specialization: Data Science (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115235 | Practice of Research (Project, 1st Semester) - Oral Exam | WT+ST | 3 | Beckert |
| T-INFO-115233 | Practice of Research (Project, 1st Semester) - Presentation | WT+ST | 3 | Beckert |
| T-INFO-115234 | Practice of Research (Project, 1st Semester) - Project Proposal | WT+ST | 4 | Beckert |
The aim of “Practice of Research" is to acquire both subject-specific knowledge and methodological competences for scientific work, and to test them on an actual research project.
After completing all four modules of “Practice of Research,” participants can:
• conduct a literature search starting from a given topic, independently identify, locate, classify, evaluate and analyze relevant literature,
• present the results of the literature search to a specialist audience in their own words using self-created presentation slides, and discuss them critically,
• formulate, delimit and articulate the relevance of a research question or research problem,
• explain the basics of the philosophy of science and relate them to their project,
• discuss the fundamentals of the employed research approach (e.g., experimental design and execution) and apply them to their project,
• design, justify, evaluate and place their own research (sub-)approach,
• derive concrete work steps and a project plan from the research question and the chosen approach,
• determine workloads, coordinate tasks and, if applicable, allocate them within a team,
• identify and analyze risk factors and develop and plan countermeasures,
• conduct scientific work in the project’s research field,
• identify, plan and carry out the necessary preparatory work for the project,
• work scientifically in the project’s research area,
• know, apply the relevant substantive foundations for the project and assess their relevance to the research question,
• document, summarize and present their planning and project progress,
• recognize and evaluate progress and develop and apply steering measures,
• name, explain and apply techniques of scientific work and scientific writing,
• plan, prepare and evaluate scientific publications,
• document, summarize and illustrate the project process and results,
• work scientifically in collaboration with others or in a team.
The content of “Practice of Research” is the guided execution of a scientific research project. Over a total period of two semesters, intensive and continuous work on the project takes place. Within the framework established by “Practice of Research,” students acquire both subject knowledge and methodological competence for scientific work.
The topics of the participants’ projects come from the research areas of the respective supervisors. In general, the project takes place within an ongoing research initiative, which ensures a strong integration of research and teaching.
The emphasis in the first semester is on project planning and carrying out preparatory work. The emphasis in the second semester is on project execution and presentation of results.
At the end of “Practice of Research” (end of the second semester), participants write a scientific paper on the project results. This work should meet the quality standards of a scientific publication and, if possible, be published.
Participation in “Practice of Research” also serves as preparation for a Master’s thesis for which the scholarly level may go beyond the usually expected scope.
In parallel to the project work, there are accompanying courses in which competencies for scientific and project-oriented work are taught (these are credited as cross-disciplinary qualifications; see the modules “Practice of Research (Methods, 1st semester)” and “Practice of Research (Methods, 2nd Semester)”). The topics of these accompanying courses are:
• scientific research methods;
• methodical search for related works on a research topic, in particular literature search, basic understanding of scientific literature;
• presentation of scientific works;
• teamwork in scientific contexts;
• methodical creation of work plans for scientific projects;
• methodical evaluation of scientific works;
• strategies for conducting scientific projects;
• creating scientific publications.
• This module forms a unit with the modules “Practice of Research (Project, 2nd Semester)”, “Practice of Research (Methods, 1st Semester)” and “Practice of Research (Methods, 2nd Semester)”. Across the four modules, a continuous research project is conducted over two semesters.
• This module can be credited either in an area of specialization or in the elective area. The corresponding mapping of offered projects to specialization areas will be announced at the beginning of the semester.
• This module includes lecture credit points, practical course credit points and seminar credit points. The practical course points cover practical scientific work under supervision; the seminar points cover the independent study and (written and oral) presentation of external scientific works; the lecture points cover acquiring substantive knowledge through reading, listening, etc. The distribution of the module’s credit points among the different types of credits will be announced at the beginning of the first semester for each project (where the modules “Practice of Research (Project, 1st Semester)” and “Practice of Research (Project, 2nd Semester)” together have at least 5 lecture credits, at least 3 seminar credits and at least 3 practical course credits).
The module can also be provided in German if needed.
The total workload for this module is 300 hours (the total workload for all four modules of “Practice of Research” is 720 hours).
The distribution of workload across the various phases and steps is project-dependent and will be announced at the beginning of the first semester.
- Attendance time in lectures and discussions: 8 hours
- Literature research and drafting the written work: 72 hours
- Practical project work individually and in a team: 136 hours
- Creation of the project proposal: 72 hours
- Exam preparation: 12 hours
Total: 300 hours (= 10 credit points)
| Coordinators: |
Prof. Dr. Bernhard Beckert
Prof. Dr.-Ing. Michael Beigl
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2026)
Area of Specialization: Algorithm Engineering (Usage from 10/1/2026) Area of Specialization: Cryptography and Security (Usage from 10/1/2026) Area of Specialization: Design of Embedded Systems and Computer Architectures (Usage from 10/1/2026) Area of Specialization: Telematics (Usage from 10/1/2026) Area of Specialization: Robotics and Automation (Usage from 10/1/2026) Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Area of Specialization: Software Engineering and Compiler Construction (Usage from 10/1/2026) Area of Specialization: Data Science (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115236 | Practice of Research (Project, 2nd Semester) - Oral Exam | WT+ST | 3 | Beckert |
| T-INFO-115237 | Practice of Research (Project, 2nd Semester) - Presentation | WT+ST | 3 | Beckert |
| T-INFO-115238 | Research Project (Project, 2nd Semester) - Scientific Report | WT+ST | 4 | Beckert |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
The aim of “Practice of Research" is to acquire both subject-specific knowledge and methodological competences for scientific work, and to test them on an actual research project.
After completing all four modules of “Practice of Research,” participants can:
• conduct a literature search starting from a given topic, independently identify, locate, classify, evaluate and analyze relevant literature,
• present the results of the literature search to a specialist audience in their own words using self-created presentation slides, and discuss them critically,
• formulate, delimit and articulate the relevance of a research question or research problem,
• explain the basics of the philosophy of science and relate them to their project,
• discuss the fundamentals of the employed research approach (e.g., experimental design and execution) and apply them to their project,
• design, justify, evaluate and place their own research (sub-)approach,
• derive concrete work steps and a project plan from the research question and the chosen approach,
• determine workloads, coordinate tasks and, if applicable, allocate them within a team,
• identify and analyze risk factors and develop and plan countermeasures,
• conduct scientific work in the project’s research field,
• identify, plan and carry out the necessary preparatory work for the project,
• work scientifically in the project’s research area,
• know, apply the relevant substantive foundations for the project and assess their relevance to the research question,
• document, summarize and present their planning and project progress,
• recognize and evaluate progress and develop and apply steering measures,
• name, explain and apply techniques of scientific work and scientific writing,
• plan, prepare and evaluate scientific publications,
• document, summarize and illustrate the project process and results,
• work scientifically in collaboration with others or in a team.
The content of “Practice of Research” is the guided execution of a scientific research project. Over a total period of two semesters, intensive and continuous work on the project takes place. Within the framework established by “Practice of Research,” students acquire both subject knowledge and methodological competence for scientific work.
The topics of the participants’ projects come from the research areas of the respective supervisors. In general, the project takes place within an ongoing research initiative, which ensures a strong integration of research and teaching.
The emphasis in the first semester is on project planning and carrying out preparatory work. The emphasis in the second semester is on project execution and presentation of results.
At the end of “Practice of Research” (end of the second semester), participants write a scientific paper on the project results. This work should meet the quality standards of a scientific publication and, if possible, be published.
Participation in “Practice of Research” also serves as preparation for a Master’s thesis for which the scholarly level may go beyond the usually expected scope.
In parallel to the project work, there are accompanying courses in which competencies for scientific and project-oriented work are taught (these are credited as cross-disciplinary qualifications; see the modules “Practice of Research (Methods, 1st semester)” and “Practice of Research (Methods, 2nd Semester)”). The topics of these accompanying courses are:
• scientific research methods;
• methodical search for related works on a research topic, in particular literature search, basic understanding of scientific literature;
• presentation of scientific works;
• teamwork in scientific contexts;
• methodical creation of work plans for scientific projects;
• methodical evaluation of scientific works;
• strategies for conducting scientific projects;
• creating scientific publications.
• This module forms a unit with the modules “Practice of Research (Project, 1st Semester)”, “Practice of Research (Methods, 1st Semester)” and “Practice of Research (Methods, 2nd Semester)”. Across the four modules, a continuous research project is conducted over two semesters.
• This module can be credited either in an area of specialization or in the elective area. The corresponding mapping of offered projects to specialization areas will be announced at the beginning of the semester.
• This module includes lecture credit points, practical course credit points and seminar credit points. The practical course points cover practical scientific work under supervision; the seminar points cover the independent study and (written and oral) presentation of external scientific works; the lecture points cover acquiring substantive knowledge through reading, listening, etc. The distribution of the module’s credit points among the different types of credits will be announced at the beginning of the first semester for each project (where the modules “Practice of Research (Project, 1st Semester)” and “Practice of Research (Project, 2nd Semester)” together have at least 5 lecture credits, at least 3 seminar credits and at least 3 practical course credits).
The module can also be provided in German if needed.
The total workload for this module is 300 hours (the total workload for all four modules of “Practice of Research” is 720 hours).
The distribution of workload across the various phases and steps is project-dependent and will be announced at the beginning of the first semester.
- Attendance time in lectures and discussions: 6 hours
- Practical project work, individually and in a team: 220 hours
- Preparation of the paper: 62 hours
- Exam preparation: 12 hours
Total: 300 hours (= 10 credit points)
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110989 | Privacy Enhancing Technologies | ST | 6 | Strufe |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
This course will provide students with a basic understanding of privacy risks, the most common technologies to tackle them and the human factors shaping their design. The course will analyze the adversary models and evaluation metrics underlying the design of privacy-enhancing technologies
• The students have a critical reasoning about privacy,
• have knowledge in the evaluation of privacy risks,
• understand the design aspects of privacy-enhancing technologies,
• are familiar with the latest research in the field
• are able to analyze and discuss the space of solutions to a given privacy problem
The following topics will be covered
• Freedom of information, the surveillance economy, and other motivations for privacy
• Privacy metrics and adversary models
• Anonymous communications
• Data-perturbative privacy-enhancing technologies
• Anonymization algorithms for databases
• Homomorphic encryption and zero knowledge proofs
• Selective disclosure for identity management
• Usable privacy
• Applying privacy principles and case studies
Attendance time in lectures: 45 h
Preparation and follow-up of the same: 90 h
Exam preparation and attendance in the same: 45 h
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114196 | Probability and Computing | Irreg. | 5 | Bläsius, Katzmann, Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students
- understand when and why randomisation is useful or necessary for solving an algorithmic problem,
- can explain central design methods and analysis tools of randomised algorithms,
- can design and explain simple randomised algorithms and data structures for solving a problem,
- can decide which tools are suitable for the analysis of given randomised algorithms and data structures and apply them.
Randomised algorithms and data structures make their approach dependent on random experiments. While the design of deterministic algorithms is often driven by a pessimistic view of worst-case behaviour, randomised algorithms rely on approaches that occasionally fail but usually perform much better.
The runtime of such algorithms as well as the solution quality (in the case of optimisation problems) and sometimes also the correctness (in the case of computational problems) are then subject to chance. A formal analysis therefore focusses on expected values and probabilities of success. We will look at classical examples as well as current research topics from the field of hashing and graph theory. Specific design methods (such as probability amplification) and advanced analysis tools of probability theory (such as coupling, Poissonisation and concentration bounds) will be applied. It will often turn out that randomised approaches are more efficient or simpler than all (or at least all known) deterministic approaches.
We will also briefly consider on the theoretical side how randomised complexity classes relate to known classes such as P and NP, and on the practical side we will clarify how randomised algorithms can be implemented on common (essentially deterministic) computers with pseudorandomness.
Lecture with exercise with 3 SWS, 5 LP
approx. 45h attendance of the lecture and exercise
approx. 30h preparation and follow-up work
approx. 45 hours working on the exercise sheets
approx. 30h exam preparation
Basic knowledge of algorithms and data structures (e.g. from the lectures Algorithms 1 + 2) as well as basic knowledge of probability theory (e.g. from the lecture Introduction to Stochastics) are helpful.
| Coordinators: |
Dr. Sebastian Kasper-Schöniger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Minor Studies: Law
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113381 | Public International Law | ST | 3 | Zufall |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Competency Goals:
- Participating students will be able to navigate the plethora of multilateral treaties to detect relevant international law for specific cases.
- They can develop solutions for legal problems based on case law of international courts and tribunals.
- Students will be able to read and comprehend international treaties and case law.
- They will have a fundamental understand of the interplay between various subfields of public international law.
- Students can identify and explain current issues in public international law.
The lecture is designed to provide participating students with a general understanding of the foundations, subjects, and sources of public international law, its interplay with national legal regimes, and more detailed knowledge of particular subfields of public international law.
Since the lecture targets students of information systems, particular focus will be given to economic topics in international law, such as investment and trade law aspects. Due to the general importance of climate change for todays (economic) law, international climate change law and environmental law will form further focus areas.
In addition, a concise overview on human rights law, the law on State responsibility, and the peaceful settlement of disputes will be provided.
Throughout the lecture, important case law will be referenced and students are expected to read relevant cases in part to facilitate a discussion of such cases and their relevance for a subject field. Although the United Nations, including its principal judicial organ, the International Court of Justice, is one of the, if not the, key international organization in public international law, further international organizations (eg, Council of Europe, World Trade Organization) and their respective law(s) will also be touched.
Students are advised to have a statute book at hand that includes the most important international treaties and conventions (eg, Evans, Blackstone’s International Law Documents, currently 15th ed 2021).
Conducting the lecture in English intends to facilitate students to link their ideas and arguments to current debates in international law.
90h
- General knowledge of (public) law (eg, through participating in public law or EU law modules) is helpful but not necessary.
- Interest in international affairs and politics is welcomed.
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111255 | Reinforcement Learning | WT | 6 | Lioutikov, Neumann |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- Students are able to understand the RL problem and challenges.
- Students can differentiate between different RL algorithm and understand their underlying theory
- Students will know the mathematical tools necessary to understand RL algorithms
- Students can implement RL algorithms for various tasks
- Students understand current research questions in RL
Reinforcement Learning (RL) is a sub-field of machine learning in which an artificial agent has to interact with its environment and learn how to improve its behaviour by trial and error. For doing so, the agent is provided with an evaluative feedback signal, called reward, that he perceives for each action performed in its environment. RL is one of the hardest machine learning problems, as, in contrast to standard supervised learning, we do not know the targets (i.e. the optimal actions) for our inputs (i.e. the state of the environment) and we also need to consider the long-term effects of the agent’s actions on the state of the environment. Due to recent successes, RL has gained a lot of popularity with applications in robotics, automation, health care, trading and finance, natural language processing, autonomous driving and computer games. This lecture will introduce the concepts and theory of RL and review current state of the art methods with a particular focus on RL applications in robotics. An exemplary list of topics is given below:
• Primer in Machine Learning and Deep Learning
• Supervised Learning of Behaviour
• Introduction in Reinforcement Learning
• Dynamic Programming
• Value Based Methods
• Policy Optimization and Trust Regions
• Episodic Reinforcement Learning and Skill Learning
• Bayesian Optimization
• Variational Inference, Max-Entropy RL and Versatility
• Model-based Reinforcement Learning
• Offline Reinforcement Learning
• Inverse Reinforcement Learning
• Hierarchical Reinforcement Learning
• Exploration and Artificial Curiosity
• Meta Reinforcement Learning
Approximately 180 hours, divided into:
• 45 hours of lecture attendance
• 15 hours of exercise attendance
• 90 hours of post-processing and working on exercise sheets
• 30 hours of exam preparation.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101387 | Reliable Computing I | Irreg. | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The objective of this course is to become familiar with general and state of the art techniques used in design and analysis of fault-tolerant digital systems.
The objective of this course is to become familiar with general and state of the art techniques used in design and analysis of fault-tolerant digital systems. The students will study and investigate existing fault-tolerant systems. Both Hardware and software methods will be studied and new research topics will be investigated.
This course overviews reliable (fault-tolerant) computing and the design and evaluation of dependable systems, and provides a base for research in reliable systems. Models and methods are used in the analysis and design of fault-tolerant and highly reliable computer systems will be taught in this course. Topics include faults and their manifestations, fault/error modeling, reliability, availability and maintainability analysis, system evaluation, performance-reliability trade-offs, system level fault diagnosis, hardware and software redundancy techniques, and fault-tolerant system design methods.
2 SWS: (2 SWS + 1,5 x 2 SWS) x 15 + 15 h preparation for the exam = 90 h = 3 ECTS
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113400 | Research Focus Class: Blockchain & Cryptocurrencies | Irreg. | 3 | Hartenstein |
| T-INFO-113401 | Research Focus Class: Blockchain & Cryptocurrencies - Seminar | Irreg. | 3 | Hartenstein |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
• Students are familiar with current issues in the field of blockchain and cryptocurrencies and can identify specific research questions.
• Students have the necessary basic knowledge to identify, discuss and scientifically address current issues in the subject area.
• Students are able to independently develop a research topic and find and process related literature.
• Students are familiar with research methods in the field of decentralized systems and have gained initial experience in a specific research topic.
• Students can write a paper according to scientific standards.
• Students can present and discuss a research topic in a colloquium.
Blockchains such as Ethereum are decentralized systems that are currently receiving a lot of attention both in practice and in research. These systems can not only be used to carry out payment transactions in a decentralized manner, but also to programmatically record and enforce processes between mutually distrustful parties in so-called smart contracts. In particular, security and fairness properties as well as scalability in terms of transaction throughput play a key role.
This course begins with a lecture in which the basics of blockchains and Ethereum in particular are taught and current problems are introduced. After an introduction to the structure and functionality of Ethereum, advanced aspects that are necessary to address current research questions will be covered. The basics of scientific methodology in dealing with decentralized systems are also covered. The basic knowledge imparted in the lecture will be applied and consolidated in the seminar - the second part of the course - through the students' own research work.
The seminar offers the opportunity to work on a self-chosen topic in the field of blockchains and cryptocurrencies, which is facilitated by the previous lecture and direct consultation. The students' task is to find and process literature on the chosen topic and to work on the chosen topic. The results are documented in a paper according to scientific standards and presented in a colloquium.
Places are limited. Information about the registration process is given in the first lecture. Registration is usually carried out via CampusPlus or Wiwi-Portal. A listing in one of them indicates that the module is offered in the current term.
6 ECTS = 180 hours
- Lecture attendance and discussion (20 hours)
- Lecture preparation and follow-up (20 hours)
- Literature research (20 hours)
- Implementation of self-chosen project (60 hours)
- Writing a scientific report (60 hours)
Knowledge from ‘Decentralized Systems: Fundamentals, Modeling, and Applications’ [M-INFO-105334] and skills from ‘Scientific Methods to Design and Analyze Secure Decentralized Systems’ [M-INFO-105780] are of advantage.
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 4/1/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 4/1/2026) Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115118 | Research Practical Course on Behavior Foundation Models | ST | 6 | Lioutikov |
See Partial achivements (Teilleistung).
See Partial achivements (Teilleistung).
Students learn to understand and scrutinise complex scientific topics and to reproduce and check published results.
and to reproduce and verify published results. Students gain in-depth knowledge in the field of interactive learning and experience with the use of novel learning methods.
Each student will select a topic in the field of Behavior Foundation Models. The organizers will suggest topics but the students are welcome suggest relevant topics. The students will then implement and evaluate several algorithms corresponding to the chosen topic. The experimental evaluation will be documented in a report and presented to their peers.
It is highly recommended to take this research project in combination with the “Seminar on Behavior Foundation Models, where the students get the chance to acquire the required background on the literature.
Workload = 180h = 6 ECTS
- Attendance time: 15h
- Project work: 135h
- Writing scientific report + preparing presentation: 30h
We highly recommend to take this research project in combination with the Seminar on Behavior Foundation Models.
It is highly recommended to attend the “Explainable Artificial Intelligence” lecture in parallel or prior to this project.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113759 | Research Practical Course: Artificial Intelligence & Security | WT+ST | 6 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualifikationsziele: Students understand how to interpret results from state-of-the-art research and are able to actively contribute to timely research.
Lernziele:
← Students know and understand concepts of recent research at the intersection of artificial intelligence and computer security.
← Students are able independently research topics and methods in this field of research.
← Students understand limits of current approach in computer security research.
In this practical course, the students work on a project at the intersection of artificial intelligence, machine learning, and computer security. They come in contact with and participate in timely and state-of-the-art research in this exciting field. In this scope, the students read up on a sub-field, design and implement a learning-based system, and conduct evaluations on real-world data.
Topics include but are not limited to adversarial machine learning, explainability of machine learning in computer security, learning-based attack detection, and vulnerability discovery.
- 140h Project work
- 20h Final report
- 15h Preparation of final presentation
- 5h attendance time (final event)
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113114 | Research Practical Course: Explainable Artificial Intelligence | WT | 6 | Lioutikov |
See partial achievements (Teilleistung).
See partial achievements (Teilleistung).
Students learn to understand and question complex scientific topics and to reproduce and verify published results.
to reproduce and review published results. Students gain in-depth knowledge of interactive learning and experience with the use of novel learning methods.
Each student will select a topic in the field of Explainable Artificial Intelligence and study and analyze it in the context of Robot Learning. The organizers will suggest topics but the students are welcome suggest relevant topics. The students will then implement and evaluate several algorithms corresponding to the chosen topic. The experimental evaluation will be documented in a report and presented to their peers.
It is highly recommended to take this research project in combination with the “Explainable Artificial Intelligence” Seminar, where the students get the chance to acquire the required background on the literature.
It is only possible to resign within two weeks after assignment of the topic.
Workload = 180h = 6 ECTS
- Attendance time: 15h
- Project work: 135h
- Write scientific report + prepare presentation: 30h
We recommend taking this Praktikum after attending the “Explainable Artificial Intelligence” lecture in the summer semester.
We highly recommend to take this research project in combination with the “Explainable Artificial Intelligence” seminar.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114203 | Research Project Deep Learning for Robotics | WT | 6 | Neumann |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to understand and scrutinise complex scientific topics and to reproduce and verify published results. Students gain in-depth knowledge in the field of learning with robots and experience with the use of novel learning methods.
Each student has to choose one of the offered topics from the area of deep learning / robot learning / deep reinforcement learning / deep imitation learning. The students need to implement one or several algorithms and evaluate them against available baselines on standard benchmark tasks as well as on (custom-made) physically realistic simulations and/or a real robot platform. The experiments have to be documented in a report. Students will work in teams of 2. It is recommended to take this course together with the seminar “Deep learning for robotics” where the students will acquire the required background on the literature.
Workload: 180h
Attendance time: 15h
Project work: 135h
Writing a report + preparing a presentation: 30h
- Experience in Machine Learning is recommended.
- Python experience is recommended
- We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114189 | Research Project: Generative AI for Autonomous Agents | ST | 6 | Neumann |
See partial achievements (Teilleistung)
Students will learn to understand, question, and reproduce complex scientific topics and published results. They will gain in-depth knowledge in the field of learning for decision making and experience with the application of novel learning methods.
This practical research course explores advanced machine learning methods and generative AI such as diffusion models to empower autonomous agents with intelligent decision-making capabilities. Students will delve into:
• Generative Models for Decision Making
• Reinforcement Learning (RL)
• Imitation Learning
• Multi-Agent Systems
• Uncertainty Quantification
• Learning Prediction Models of Physical Processes
• Time-Series Modeling
• Discovery and Inference of Latent Variables
Each student will choose one of the offered topics, implement one or several algorithms, and evaluate them against available baselines using standard benchmark tasks. The course emphasizes hands-on experimentation, requiring students to document their findings in a detailed report. Students will work in teams of two, closely collaborating with their supervisor with the aim of achieving publishable results. This course provides students with their first experience in running a research project in machine learning, including algorithm design, evaluation, benchmarking, deploying algorithms on HPC hardware, and paper writing.
Workload: 180h
Attendance time: 15h
Project work: 135h
Writing a report + preparing a presentation: 30h
- Experience in Machine Learning is recommended.
- Python experience is recommended
- We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111209 | Resilient Networking | WT | 6 | Strufe |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
This course will provide students with a basic understanding of threats to the Internet, and the most common technologies to tackle them. The course will analyze the adversary models and evaluation metrics underlying their design.
The lecture resilient networking provides an overview on the basics of secure networks as well as on current threats and respective countermeasures. Especially bandwidth-depleting Denial of Service attacks represent a serious threat. Moreover, over the last years the number of targeted and highly sophisticated attacks on company and governmental networks increased. To make it worse, as a new trend at the moment, the interconnection of the Internet with cyber physical systems takes place. Such systems, e.g., the energy network (smart grid), trans- portation systems and large industrial facilities, are critical infrastructures with severe results in case of their failure. Thus, the Internet that interconnects these systems has evolved to a critical infrastructure as well.
The lecture introduces the current state-of-the-art in the research towards resilient networks. Resilience-enhancing techniques can be generally classified in proactive and reactive methods. Proactive techniques are redundancy and compartmentalization. Redundancy allows to tolerate attacks to a certain extent, while compartmentalization attempts to restrict the attack locally and preventing its expansion across the whole system. Reactive techniques follow a three step approach by comprising the phases of detecting an attack, mitigate its impacts, and finally restore a system's usual operation.
Based upon this categorisation of resilience strategies the lecture will give an excursus to graph theorie and will introduce generic strategies to increase the resilience of networks, e.g., proactively establishing backup routes and fast restoration strategies. Furthermore, the lecture will provide an overview on BGP routing and the Domain Name Service, as two essential Internet services. Both services are presented and current attacks as well as corresponding countermeasures are described. Moreover, Denial of Service attacks and their mitigation are observed in detail as well as mechanism for increasing the resilience of P2P networks. Finally, Intrusion Detection systems are covered as mechanisms to mitigate the impacts of successful attacks.
Knowledge of the basics of cryptography and computer networks is helpful.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
(Usage from 4/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-114952 | Robotic Intelligence: Navigation and Mobile Manipulation | ST | 6 | Rönnau |
See module component
See module component
Students acquire a solid understanding of fundamental principles and current methods in robotics, including navigation, legged locomotion, and manipulation with mobile systems. They can apply suitable techniques from machine learning, simultaneous localisation and mapping (SLAM), decision-making, and collision-free path planning to autonomously traverse complex real-world environments—for example, with a multi-legged robot. A particular emphasis is placed on the direct physical interaction of legged robots with terrain and obstacles. Students learn to design suitable machine learning approaches for locomotion and to train them in dynamic simulation environments, enabling robots to perform movements on simple, flat terrain and to overcome complex obstacles such as stairs.
Furthermore, students gain knowledge of various manipulation techniques, including robotic grasping and object handling. They can analyse and compare both classical manipulation approaches and modern machine-learning-based methods, and evaluate their performance. Combined, these capabilities enable mobile robots to navigate challenging terrain and autonomously manipulate objects at their destination.
The lecture Robotic Intelligence: Navigation and Mobile Manipulation provides fundamental and advanced concepts for developing intelligent, autonomous robots for complex environments. The focus is on methods for navigation, legged locomotion, and mobile manipulation, as well as their integration into real robotic systems. Covered topics include sensor fusion, localization and mapping (SLAM), decision-making, and collision-free motion and path planning. Additionally, machine learning techniques for controlling legged robots in challenging terrain and for direct physical interaction with the environment are introduced. The course further addresses concepts of mobile manipulation, enabling robots to safely grasp, handle, or interact with objects and their surroundings. Both classical and modern, learning-based approaches are presented and discussed. By combining these methods, robotic systems can achieve a high level of autonomy. The lecture integrates theoretical foundations with application-oriented methods and enables students to develop and critically assess autonomous robotic systems and their methods for real-world scenarios.
An important component of the course is the practical group work conducted in the Machine Intelligence and Robotics Lab (MaiRo), where students solve tasks using real sensors and robots. They develop both technical competencies and teamwork skills to address complex navigation and manipulation challenges of mobile systems.
The topics include:
By the end of the course, students can design, implement, and integrate methods for navigation and mobile manipulation. These approaches enable mobile systems to autonomously navigate through complex, dynamic environments, apply learned locomotion strategies, and perform various manipulation tasks.
Written exam: duration 60 minutes. A grade bonus can be achieved by successfully completing the exercises.
See further details on course organization at: teaching.imi.kit.edu and in ILIAS.
180 hours total, including approximately:
Vorlesung, Übung und Gruppenarbeit
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
(Usage from 4/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-114813 | Robotic Intelligence: Perception and Scene Understanding | WT+ST | 6 | Rönnau |
See module component
See module component
The student will understand the fundamental principles and state-of-the-art methods for robotic perception and scene understanding. They will gain the ability to select and apply suitable machine learning and computer vision techniques to interpret complex real-world environments, from raw sensory data to structured scene representations. The student will be able to design perception pipelines that combine multiple sensing modalities, enabling robust object recognition, tracking, segmentation, and 3D scene analysis. Furthermore, they will be able to critically assess and adapt these methods for robotic applications that require situational awareness and informed decision-making.
The lecture Robotic Intelligence: Perception and Scene Understanding combines modern AI-based perception methods with the engineering skills needed to apply them in robotics. It covers the full perception chain, from acquiring and calibrating sensor data to interpreting scenes and integrating perception modules into robotic systems, like mobile walking robots.
Students will first build a foundation in machine learning and computer vision, covering image classification, object detection, tracking, and segmentation in both 2D and 3D. The course then explores advanced deep learning architectures, including Vision Transformers, multimodal perception systems, and end-to-end multimodal machine learning pipelines. Special emphasis is placed on techniques for integrating vision, depth, and other sensing modalities, enabling robust perception in challenging real-world environments.
A key part of the course is practical group work in the Machine Intelligence and Robotics Lab (MaiRo), where students solve hands-on tasks using real sensors and robots, building both technical competence and teamwork skills.
Topics include:
By the end of the course, students will be able to design, implement, and integrate intelligent robotic perception systems that enable mobile robots to understand and act within complex, dynamic environments.
Written exam: duration 60 minutes. A grade bonus can be achieved by successfully completing the exercises.
180 working hours, of which approx:
Lecture, exercise and group work
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114172 | Practical Course: Robotics | ST | 6 | Asfour |
See partial Achievements (Teilleistung)
See partial Achievements (Teilleistung)
The student knows concrete solutions for different problems in robotics. He/she uses methods of inverse kinematics, grasp and motion planning, and visual perception. The student can implement solutions in the programming languages C++ and Python with the help of suitable software frameworks.
The practical course is offered as an accompanying course to the lectures Robotics I-III. Every week, a small team of students will work on solving a given robotics problem. The list of topics includes robot modeling and simulation, inverse kinematics, robot programming via state charts, collision-free motion planning, grasp planning, robot vision and robot learning.
Practical course with 4 SWS, 6 LP
6 LP corresponds to 180 hours, including
2 hours introductory event
18 hours initial familiarization with the software framework
120 hours group work
40 hours attendance time
Attending the lectures Robotics I – Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics and Mechano-Informatics and Robotics is recommended.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114190 | Robotics I - Introduction to Robotics | WT | 6 | Asfour |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
The students are able to apply the presented concepts to simple and realistic tasks from robotics. This includes mastering and deriving the mathematical concepts relevant for robot modeling. Furthermore, the students master the kinematic and dynamic modeling of robot systems, as well as the modeling and design of simple controllers. The students know the algorithmic basics of motion and grasp planning and can apply these algorithms to problems in robotics. They know algorithms from the field of image processing and are able to apply them to problems in robotics. They are able to model and solve tasks as a symbolic planning problem. The students have knowledge about intuitive programming procedures for robots and know procedures for programming and learning by demonstration.
The lecture provides an overview of the fundamentals of robotics using the examples of industrial robots, service robots and autonomous humanoid robots. An insight into all relevant topics is given. This includes methods and algorithms for robot modeling, control and motion planning, image processing and robot programming. First, mathematical basics and methods for kinematic and dynamic robot modeling, trajectory planning and control as well as algorithms for collision-free motion planning and grasp planning are covered. Subsequently, basics of image processing, intuitive robot programming especially by human demonstration and symbolic planning are presented.
In the exercise, the theoretical contents of the lecture are further illustrated with examples. Students deepen their knowledge of the methods and algorithms by independently working on problems and discussing them in the exercise. In particular, students can gain practical programming experience with tools and software libraries commonly used in robotics.
Lecture with 3 SWS + 1 SWS Tutorial, 6 LP
6 LP corresponds to 180 hours, including
15 * 3 = 45 hours attendance time (lecture)
15 * 1 = 15 hours attendance time (tutorial)
15 * 6 = 90 hours self-study and exercise sheets
30 hours preparation for the exam
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114152 | Robotics II - Humanoid Robotics | ST | 3 | Asfour |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can explain the challenges and goals of humanoid robotics research, particularly regarding the implementation of complex sensorimotor and cognitive abilities in humanoid robots. They are familiar with the history of the field and understand how biomechanical models of the human body inform humanoid robot design.
Students have comprehensive knowledge of human and robotic grasping and manipulation. They can analyze human grasping and manipulation strategies, understand taxonomies, and evaluate different computational approaches for grasping and manipulation. They understand the challenges in transferring concepts from human studies to humanoid robots.
Students understand fundamentals of learning from human demonstration and imitation learning. They can explain the learning from demonstration cycle and methods for learning generalized task representation, especially task constraints from demonstrations, and how learned behaviors are reproduced on robots.
Students can describe cognitive and AI-based architectures for humanoid robots, including approaches to address the signal-to-symbol gap and human-inspired memory architectures that enable intelligent robot behavior.
This lecture addresses the implementation of complex sensorimotor and cognitive abilities in humanoid robots, with humans serving as inspiration. The lecture begins by addressing the motivation for humanoid robotics. The history of humanoid robotics and biomechanical models of the human body that inform robot design are covered, along with some mechatronic principles underlying humanoid robot systems.
The first main topic comprehensively addresses grasping and manipulation. After covering fundamentals, the lecture discusses neuroscientific insights and concepts like grasp phases and grasping synergies to reduce control complexity. Taxonomies are presented as structured frameworks for understanding the space of possible constraints in grasping and manipulation. The lecture addresses computational approaches for grasping known, familiar, and unknown objects, covering both classical methods and modern learning-based approaches using deep learning and vision-language-action models.
The second main topic covers learning from demonstration and imitation learning. After introducing the topic, fundamentals are presented including the learning from demonstration cycle. The lecture emphasizes learning task models on both symbolic/semantic and subsymbolic/sensorimotor levels. While methods for capturing human demonstrations are briefly covered, the focus is on semantic segmentation of human demonstrations and learning of task constraints that capture the task and allow generalization. Movement primitives are presented as efficient representations that allow robots to generalize learned behaviors to new situations.
The lecture concludes with cognitive and AI-based architectures for humanoid robots, discussing state-of-the-art approaches, methods to address the signal-to-symbol gap, and human-inspired memory architectures that enable intelligent robot behavior.
Lecture with 2 SWS, 3 ECTS.
3 ECTS corresponds to approx. 90 hours, thereof:
approx. 15 * 2h = 30h attendance time
approx. 15 * 2h = 30h self-study prior/after the lecture
approx. 30h preparation for the exam and exam itself
Having visited the lecture on Robotics I – Introduction to Robotics is recommended
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114155 | Robotics III - Sensors and Perception in Robotics | ST | 3 | Asfour |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
Students can explain the main sensor principles used in robotics and distinguish between proprioceptive sensors (encoders, force/torque sensors, IMUs) and exteroceptive sensors (proximity, range, visual, and tactile sensors). They understand and can characterize sensor properties such as resolution, range, accuracy, and bandwidth, and can explain the principles of analog-to-digital conversion. Students are able to propose, analyze, and justify suitable sensor concepts for specific robotic tasks, taking into account trade-offs between different sensor modalities.
Students can apply fundamental perception methods for robotic applications. This includes robot vision techniques such as feature detection and matching, object detection and recognition, and semantic segmentation, including deep learning-based approaches. They can describe and analyze perception methods for manipulation tasks, including object pose estimation, grasp detection, active vision strategies, and haptic exploration. In addition, students can explain point cloud processing methods for registration and surface reconstruction, as well as principles of simultaneous localization and mapping (SLAM).
This lecture complements the lecture “Robotics I” and provides a comprehensive overview of sensors and perception methods in robotics. It is divided into two main parts.
The first part introduces fundamental concepts of perception in robotics, including the distinction between sensation and perception within the perception-cognition-action loop. It covers sensor fundamentals, such as sensor characteristics (resolution, range, accuracy, bandwidth), analog-to-digital conversion, and common sensor classification schemes. Proprioceptive sensors for measuring the robot’s internal state are discussed, including encoders (optical, magnetic, absolute, and incremental), force/torque sensors, and inertial measurement units (IMUs). The lecture then covers exteroceptive sensors, including proximity sensors, range sensors (LiDAR, time-of-flight cameras, ultrasonic sensors), visual sensors (monocular, stereo, RGB-D cameras), and tactile sensing technologies (capacitive, resistive, and optical).
The second part focuses on processing and interpreting sensor data. Topics in robot vision include image representation, feature detection and matching, object detection and recognition, semantic segmentation, and corresponding deep learning approaches. Point cloud processing is covered with emphasis on data structures, registration, surface reconstruction, and semantic segmentation. The lecture also addresses perception for manipulation, covering object pose estimation, grasp detection, visual servoing, active vision, and haptic exploration. It concludes with the simultaneous localization and mapping (SLAM) problem, including EKF SLAM, Graph SLAM, and FastSLAM.
Lecture with 2 SWS, 3 ECTS.
3 ECTS corresponds to approx. 90 hours, thereof:
approx. 15 * 2h = 30h attendance time
approx. 15 * 2h = 30h self-study prior/after the lecture
approx. 30h preparation for the exam and exam itself
Having visited the lectures on Robotics I – Introduction to Robotics is recommended.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
TT-Prof. Dr. Barbara Bruno
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114895 | Robotics Institute Germany - Lecture Series | WT | 3 | Asfour, Bruno, Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can discuss the foundations of various open research directions related to AI-powered robotics, as well as their open challenges, relevant findings/breakthroughs and state-of-the-art approaches.
The course is structured as an inter-institutional lecture series, concurrently offered by various German institutions affiliated with the Robotics Institute Germany (RIG). In the lecture series students are offered an overview of significant open research directions related to AI-powered robotics by leading figures in the field. The lecturers are affiliated with various RIG partner institutions and students from various RIG partner institutions attend the lectures remotely. The lectures cover topics such as cognitive robotics, robot learning, robot perception, manipulation, natural interaction and communication with robots, human-robot interaction, soft robotics and responsible robotics. The exposure to top researchers in the field from different German institutions and the possibility of exchanges with students from other German universities are unique opportunities offered by this lecture series.
Lecture series with 2 SWS, 3 LP. 3 LP corresponds to approx. 90 hours, of which: approx. 22.5 hrs. of compulsory attendance approx. 45 hours of literature research approx. 22.5 hrs. exam preparation
Knowledge of the content of module Robotics I - Introduction to Robotics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114133 | Sampling Methods for Machine Learning | ST | 6 | Hanebeck |
| T-INFO-114134 | Sampling Methods for Machine Learning - Pass | ST | 0 | Hanebeck |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students will understand and be able to implement various sampling techniques, from basic random number generation to advanced methods like normalizing flows. They will develop the ability to evaluate sampling quality, optimize procedures, and select appropriate methods for specific machine learning tasks. Graduates will be capable of independently developing sampling solutions for complex problems and critically assessing different approaches. Their comprehensive understanding will enable them to engage with current developments in the field and apply their knowledge effectively in both research and practical applications. This will be supported via a digital exercise.
Sample-based inference is the de-facto standard for solving otherwise infeasible problems in machine learning, estimation, and control under (unavoidable) uncertainties. Thus, it is an important foundation for further studies. This lecture gives a thorough overview of state-of-the-art sampling methods and discusses current developments from the research frontier.
The first part shows how to efficiently sample large numbers of random samples from given densities starting with the special cases of uniform and Gaussian distributions. For sampling from arbitrary densities, important techniques such as inverse transform sampling, Knothe-Rosenblatt maps, Markov chain Monte Carlo, normalizing flows, and Langevin equations are introduced.
The second part is concerned with deterministic or low-discrepancy sampling, where the goal is to find a set of representative samples of a given density. These are usually obtained by optimization, which, in contrast to random samples, leads to good coverage, high homogeneity, and reproducible results. To analyze and synthesize such samples, various statistical tests and discrepancy measures are presented. This includes scalar tests such as the Cramér-von Mises test, Kolmogorov-Smirnov test, and multivariate generalizations based on Localized Cumulative Distributions and Stein discrepancy.
Finally, advanced topics such as importance sampling and sampling from the posterior density in a Bayesian update are discussed. Typical applications of sample-based inference include Bayesian neural networks, information fusion, and reinforcement learning.
Per week:
2 SWS Presence
2h Follow-up
6h Digital exercise with programming tasks
2h Exam preparation
= 12h/week und 180h/semester
Knowledge of a higher programming language with sophisticated libraries for scientific-numerical computing (e.g. Julia, Matlab, Python) is advantageous.
| Coordinators: |
Prof. Dr. Henning Meyerhenke
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115282 | Scalable Graph and Sparse Matrix Algorithms for Learning | Irreg. | 5 | Meyerhenke |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students acquire a unified view of graph algorithms, sparse linear algebra, and graph learning. They learn to formulate graph computations as sparse operators, to analyze their algorithmic and architectural properties, and to develop scalable implementations for modern parallel systems.
After successful completion of the module, students are able to
The module introduces algorithmic and computational foundations for the analysis and learning of large-scale graph-structured data. A central theme is the interpretation of graph computations as sparse operators and the resulting interplay between graph algorithms, sparse linear algebra, and modern parallel architectures.
The course revisits classical graph algorithms from the perspective of propagation processes and sparse computations. It covers sparse matrix representations and kernels, graph algorithms based on sparse linear algebra, iterative methods, and spectral techniques. Advanced algorithmic topics include sparse matrix-matrix multiplication, graph coarsening, multilevel methods, and sparsification.
Building on these foundations, the course investigates the design and implementation of scalable graph and sparse matrix algorithms. Topics include parallel graph algorithms, performance engineering for sparse computations, graph partitioning, and communication-efficient computation.
Finally, the course introduces graph learning methods and relates them to the concepts developed throughout the module. Graph neural networks are studied as parameterized sparse propagation operators, with a particular focus on their computational characteristics.
1. Attendance time (Course, exercise, etc.) 45 h
2. Self-study (e.g. independent review of course material,
work on homework assignments) 75 h
3. Preparation for the exam 30 h
Basic knowledge of graph algorithms, linear algebra, and C++.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111568 | Scientific Methods to Design and Analyze Secure Decentralized Systems | WT | 6 | Hartenstein |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
1. Philosophy of Science: The student understands epistemological principles like the scientific and mathematical process, within the context of networked and decentralized systems. The student knows about the current limits of scientific research, especially with respect to the security of a given decentralized system.
2. Empirical Methods: Observation / Monitoring: The student can construct setups for observation studies of system properties related to performance or security. The student knows how to monitor a decentralized system like an overlay network without interference, i.e., without impact on the behavior to measure as well as the overall system functionality.
3. Combined Empirical / Formal Methods: The student has a fundamental understanding of Discrete Event Simulations, as well as stochastic modelling and random number generation. The student can conduct a simulation study consisting of observation, modelling, simulation, validation, and result analysis.
4. Formal Methods: The student knows how to apply formal methods like formal verification / model checking and model comparison / simulation-based proofs to decentralized systems. The student understands tradeoffs between empirical and formal methods, and can choose suitable methods for given research tasks.
5. Applications in Research: The student understands how the methods of this lecture are applied to practical examples, and knows how to apply the methods on problems of a researcher’s everyday life.
Decentralized Systems (like peer-to-peer- or blockchain-based systems) are systems controlled by multiple parties who make their own independent decisions to reach a common goal. However, not knowing which parties are trustworthy and which are betrayers requires a radically different way of thinking. Based on the lecture “Decentralized Systems: Fundamentals, Modeling, and Applications”, in this lecture, we cover the necessary scientific methods to analyze existing and to create new decentralized systems. We cover a mix of both empirical and formal methods and their tradeoffs, as well as the overarching philosophy of science behind the research process. Together with its practical parts, this lecture provides the foundational scientific toolbox to work on the decentralized systems of the future.
Lecture workload:
1. Attendance time (Course, exercise,): 4 SWS: 15 x 4h = 60h
2. Self-study (e.g. independent review of course material,
work on homework assignments)
Weekly preparation and follow-up of the lecture/exercise: 15 x 4 SWS x 1,25h = 75h
3. Preparation for the exam: 45h
Σ = 180h = 6 ECTS
Prior knowledge on the abstract concepts as well as concrete use cases of decentralized systems is strongly recommended. The “Decentralized Systems: Fundamentals, Modeling, and Applications” lecture covers all necessary aspects, but equivalent lectures and / or self-study can also be sufficient.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-105577 | Seminar Dependable Computing | WT+ST | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The objective of this seminar is to become familiar with general and state of the art techniques used in design and analysis of fault-tolerant digital systems.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Reliability plays a major role in design of contemporary and next generation electronics. In many safety-critical application domains, reliability is considered as the main design criteria. With nanoscale technologies, the reliability of individual devices is decreasing, therefore, reliable computing must be considered in the design flow in order to ensure correctness of computing.
The objective of this seminar is to become familiar with general and state of the art techniques used in design and analysis of fault-tolerant digital systems. This seminar overviews reliable (fault-tolerant) computing and the design and evaluation of dependable systems, and provides a base for research in reliable systems.
The topics include study and investigation of existing and classical fault-tolerant systems as well as current trend in the research of reliable computing. Since reliability spans from hardware to software, and from device-level to system-level, various topics can be envisioned in the scope of this seminar and the prospective students can choose specific topic from a wide range of areas based on their interests and background.
4 SWS / 3CP = 90h/week
Knowledge of “Fault Tolerant Computing” and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2026)
Area of Specialization: Robotics and Automation (Usage from 10/1/2026) Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115260 | Seminar for Practical Course: Machine Learning and Intelligent Systems | Irreg. | 3 | Hanebeck |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114268 | Seminar in Privacy | ST | 4 | Strufe |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The seminar deals with current topics from the research field of technical data protection.
These include, for example:
- Attacks on private information in behavioral data
- Anonymous communication
- Publication of anonymized usage data (semantic/syntactic privacy)
- Understanding and supporting the use of online media
- Security in networks
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The student is able to
- carry out a literature search based on a given topic, identify and evaluate the relevant literature;
- independently compile research results from IT security and technical data protection;
- analyze and discuss scientific studies and place them in their context;
- carry out their own classifications and evaluations of scientific studies, report on them in writing and present the results in a short scientific presentation.
Workload attendance time in the seminar: 10h
Research and preparation of a paper: 75h
Reviewing and commenting on the preliminary papers of fellow students: 5h
Preparing the presentation: 30h
Fundamentals of IT security, computer networks and distributed systems are required
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 4/1/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 4/1/2026) Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115119 | Seminar on Behavior Foundation Models | ST | 3 | Lioutikov |
See Partial Achivements (Teilleistung).
See Partial Achivements (Teilleistung).
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic.
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic. Students get a deeper understanding of state-of-the art learning algorithms and get to know current research challenges.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Each student will select several related papers in the field of Behavior Foundation Models. The organizers will suggest several papers but the students will be encouraged to indentify and research additional relevant papers during the semester. The students will then prepare a presentation and a basic scientific research paper.
It is highly recommended to take this seminar in combination with the Research Practical Course on Behavior Foundation Models (Forschungspraktikum), where the students get the chance to deepen their understanding, implement and evaluate their presented work.
Workload = 90 h = 3 ECTS
- Attendance time: 15hr
- Self-study: 45h
- Writing a scientific report: 20h
- Prepare presentation: 10h
We highly recommend to take this seminar in combination with the Research Practical Course on Behavior Foundation Models (Forschungspraktikum).
It is highly recommended to attend the “Explainable Artificial Intelligence” lecture in parallel or prior to this seminar.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
PD Dr. Bastian Breustedt
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-115253 | Seminar Radiation Protection in Medicine | WT | 3 | Breustedt |
The examination consists of a written handout (approx. 2-3 pages) and an oral presentation (approx. 25-35 min.) of the students work. Considered will be the quality of the contents, the style of the presentation, the quality and content of the handout. The overall impression is rated.
none
The seminar will consist of a block of 3 introductory lectures presenting medical exposures in the system of radiological protection. Applications of radiation in medicine will be presented and their specific issues regarding protection of patient, staff and public will be outlined. The second block will consist of presentations of the students on a dedicated topic, which will be selected by them a the begin of the term. Students shall look into the problem and develop a solution to the issues identified. The presentations shall introduce the topic, state the problems from a radiation protection perspective and introduce solutions and methods applied. The topic will then be discussed within the group. A final lecture will summarize the presentations of the students and offer space for a final discussion on medical radiation protection.
The module grade is the grade of the seminar contributions. Details will be given during the lecture.
For capacity reasons, the seminar is limited to 20 students. If necessary, a selection procedure will be carried out. Places will be allocated according to the progress of the students (semester and number of attended courses with topics covering radiation medicine). Details will be announced in the first course and on the course homepage.
Knowledge about of the basics of radiation protection is strongly recommanded. Information and material for self-study will be provided in the seminar.
2 SWS
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
(Usage from 10/1/2025)
Elective Studies in Informatics (Usage from 10/1/2025) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114433 | Seminar: Advanced Topics in Natural Language Processing | WT+ST | 3 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to familiarise themselves independently with topics based on scientific literature and prepare them for presentations.
From the other presentations, students gain in-depth knowledge in sub-areas of machine translation and learn to critically analyse the work presented.
Machine translation now makes it possible to automatically translate both written texts and spoken language into another language. In statistical approaches to machine translation, methods from machine learning are primarily used to train statistical models for the translation process.
In the seminar, current research results on various aspects of the systems will be discussed. Selected publications from the fields will be presented by the participants. Possible topics include improvement of word order and grammar of the target language, adaptation to topic or genre, treatment of spoken language phenomena, error correction, ...
Students are familiar with the DFG Code of Good Scientific Practice and successfully apply these guidelines when writing their scientific work.
90h
Previous knowledge from the lecture "Machine Translation" is an advantage.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114231 | Seminar: Advanced Topics on SAT Solving | WT | 3 | Iser, Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
With selected high-influence papers from the field of SAT solving, we take a close look at how SAT solvers evolved in the past decade and learn about the major cornerstones of modern and efficient large scale SAT solving systems.
Attendance time (3-4 dates): 4.5 - 6h
Reading, summarising and relating (2-3 papers): 30 - 40h
Preparation of the presentation: 16 - 24h
Total 90h
Knowledge of the basics from "SAT Solving in Practice" is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112312 | Seminar: Algorithm Engineering | Irreg. | 4 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can
- carry out a literature search based on a given topic, identify, locate, evaluate and finally analyse the relevant literature.
- prepare presentations in a scientific context. To this end, students master techniques that enable them to prepare and present the content to be presented to the audience.
- prepare their written seminar paper (as required later for further academic work) in accordance with the requirements and quality standards of academic writing, taking into account the format requirements specified by academic publishers for the publication of documents.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
This seminar covers various topics from the field of algorithm engineering. The focus can be on scalability, parallelism, efficiency or theoretical guarantees of algorithms. Example topics may include graph algorithms, sorting algorithms, string algorithms, SAT solvers, data structures or other algorithms. The exact focus of the seminar for the current semester will be announced in advance on the institute website by the chair of Prof. Sanders.
Participants in the seminar carry out their own literature research, present their results to their fellow students and prepare a paper.
The exact formalities will be announced at a kick-off event at the beginning of the semester, which will also be announced on the institute's website.
4 LP corresponds to approx. 120 working hours, of which
- 10h seminar attendance
- 45h Literature research, assessment and evaluation of relevant literature
- 25h preparation of own presentation
- 25h Preparation of the written paper
- 15h preparation and follow-up work
Knowledge of algorithms is an advantage. Exemplary lectures are Algorithms I, Algorithms II, Algorithm Engineering and Parallel Algorithms.
| Coordinators: |
TT-Prof. Dr. Benjamin Schäfer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113110 | Seminar: Artificial Intelligence for Energy Systems | WT | 4 | Schäfer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
• Students obtained a foundational knowledge of AI in energy systems as an active research field and can name some ongoing challenges
• Students are able to independently conduct a literature review on a given topic.
• Students are able to present their knowledge in a written and structured report
• Students are able to orally present results and discuss topics of the seminar in the broader context of the field
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Artificial Intelligence (AI) is a key technology in many areas of society and research. Energy systems with the ongoing energy transition (“Energiewende”) make it a fascinating field for deploying AI methods. AI and machine learning algorithms can play a crucial role in improving energy efficiency, optimizing power generation and distribution or enhancing system stability while facilitating additional renewable energy integration. This seminar will explore fundamental AI algorithms and their applications in energy systems. Examples may include forecasting of energy demand or renewable generation, explainability of algorithms as well as optimization via AI.
20h attendance time (kick-off and talks by other students)
20h literature review
40h writing of own contribution
10h per-review for other students
30h preparation of the final presentation
120h=4ECTS
Previous participation in “Energieinformatik 1” and/or “Energieinformatik 2” is beneficiary but not mandatory.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
Minor Studies: Biologically Inspired Robotics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-MACH-113842 | Seminar: Bionic Algorithms and Robot Technologies | WT+ST | 3 | Rönnau |
See partial achievement
None
The aim is to work independently on a scientific topic in the field of biologically inspired algorithms and robot technologies.
Students are able to independently carry out a literature search on the state of research, summarize external work accurately, relate it to each other and evaluate it.
The results and content can be summerized in a seminar paper and an oral presentation.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and apply these guidelines successfully in the preparation of their scientific work.
Biologically inspired robots and their methods and technologies transfer concepts for problem solving from nature to mechanical design, sensor technology, navigation, control and interpretation, among other things. These solution approaches are approximated by technical systems. The spectrum of robotics inspired by biology ranges from multi-legged walking robots, distributed sensor concepts and lightweight construction to machine learning methods and neuromorphic hardware.
90h workload
- Literature research: 35h
- Elaboration of the seminar paper: 35h
- Preparation of the final presentation: 10h
- Attendance time: Kickoff, presentation and discussion as well as meeting with supervisors: 10h
The visit of the lecture "Biologically Inspired Robots" is helpful.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110794 | Seminar: Continuous Software Engineering | ST | 4 | Koziolek |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can
- carry out a literature search based on a given topic, identify, locate, evaluate and finally analyze the relevant literature.
- prepare their seminar paper (and later their Bachelor's/Master's thesis) with a minimum of training, taking into account the format requirements specified by all publishers for the publication of documents.
- Preparing presentations in a scientific context. To this end, techniques are introduced that make it possible to prepare and present the content to be presented in an auditorium-appropriate manner.
present the results of their research in written form, as is generally the case in scientific publications.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and apply these guidelines successfully in the preparation of their scientific work.
Modern software engineering takes place in short cycles that enable rapid feedback Technologies such as build servers and containerization enable fast, frequent and automatic deployment of software in productive operation and rapid feedback into development (DevOps).
The term "Continuous Software Engineering" summarizes the interlocking of the various activities.
The seminar will examine various current challenges in the field of continuous software engineering, including the engineering of applications with machine learning components.
25 working hours for the literature research
55 working hours for the preparation of the thesis and peer reviews
20 working hours for the preparation of the final presentation
20 working hours for the final block event and meeting with the supervisor.
This results in a total of 120 working hours
| Coordinators: |
Prof. Dr. Pascal Friederich
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113915 | Seminar: Critical Topics in AI | ST | 3 | Friederich |
See partial achivements (Teilleistung)
See partial achivements (Teilleistung)
Qualification objectives:
• Students are able to work independently on literature about a topic of current research and to critically evaluate it, to find and understand relevant publications, and to classify and process their content accordingly in order to be able to present the chosen topic area in the form of a presentation and a written paper.
Learning Objectives
• Overview of positive as well as negative impact of AI technology
• Overview of scientific and related ethical issues in current AI research
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
This seminar covers the technical as well as ethical aspects of critical issues in AI. Topics covered include bias in machine learning methods, ethically and socially critical applications of AI, and the impact of AI on society. The exact topics will be determined in each semester.
Students will work independently on an advanced topic and critically engage with it, presenting and discussing their findings in a lecture and summarizing them in a seminar paper.
Total 90 h, of which:
• Introductory courses: 4 h
• Literature research: 30 h
• Writing the report (10-15 pages) and preparing the presentation (30+15 minutes): 50 h
• Presentation of the results: 6 h
Interest in social topics and research questions is required
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114091 | Seminar: Current Trends in Theoretical Computer Science | Irreg. | 4 | Künnemann-Goos |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to:
- perform a literature review on the basis of a given topic/scientific paper, to read and understand relevant scientific works in theoretical computer science and to identify the scientific context.
- present a scientific paper and its context.
- lead the discussion on the merits of the paper.
- create a written report of their topic in accordance to usual quality standards
for scientific writing
This seminar discusses current trends and topics in theoretical computer science, with a focus on algorithms & complexity theory. This includes short deep-dives into hot topics of research, as well as possibly required background material. Topics covered in this class may well inspire subsequent research projects/thesis topics.
The seminar is organized in a reading group format: Each student is expected to perform a basic reading of the topic for each session. A designated session leader (either a student or non-student participant) has prepared the material more thoroughly and leads the discussion, usually including a basic presentation. Every student participant is expected to lead a session at least once.
The sessions usually take place in blocked format (i.e., on a small number of dates).
The specific contents vary, but often focus on 2-3 main themes of current research in the field.
4 CP amounts to 120 h, distributed as follows:
- about 15 h attendance in class
- about 45 h reviewing the literature
- about 40 h preparation of presentation/discussion
- about 20 h writing of scientific report
Basic knowledge of theoretical computer science and algorithm design is recommended.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114204 | Seminar: Deep Learning for Robotics | WT | 3 | Neumann |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic.
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic. Students get a deeper understanding of state-of-the art learning algorithms and get to know current research challenges.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Each student has to choose one of the offered topics from the area of deep learning / robot learning / deep reinforcement learning / deep imitation learning. Each topic consists of several research papers for which the students have to prepare a presentation as well as a report in form of a scientific research paper. It is recommended to take the seminar together with the “Research Project Deep Learning for Robotics”, where the presented algorithms will be implemented and evaluated. Students will work in teams of 2.
Workload = 90 h = 3 ECTS
Attendance time: 15h
Self-study: 45h
Writing a scientific report: 20h
Prepare presentation: 10h
Attendance of the lecture "Machine Learning - Fundamentals and Algorithms" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114255 | Seminar: Embedded Systems I | WT+ST | 3 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn the basics of scientific work in the form of literature research, writing a scientific paper and giving a presentation to a specialist audience.
Learning objectives:
Students learn to read conference papers, articles in specialist journals and standard literature. Furthermore, they interpret these texts in order to give an overview of the topic in their own words in a paper. Finally, they also present an overview of the topic to other computer scientists. In doing so, they are trained in scientific writing in the form of expression, text structure and reduction to the essentials.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines when writing their scientific work.
This module bundles the seminars at the Chair of Embedded Systems:
Internt of Things
Machine Learning
Embedded Security and Architectures
For current information, please check the course catalog and the Chair of Embedded Systems homepage at https://ces.itec.kit.edu.
90 h
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114256 | Seminar: Embedded Systems II | WT+ST | 3 | Henkel |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification objective: Students learn the basics of scientific work in the form of literature research, writing a scientific paper and giving a presentation to a specialist audience.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines when writing their scientific work.
Learning objectives:Students learn how to read conference papers, articles in specialist journals and standard literature. Furthermore, they interpret these texts in order to give an overview of the topic in their own words in a paper. Finally, they also present an overview of the topic to other computer scientists. In doing so, they are trained in scientific writing in the form of expression, text structure and reduction to the essentials.
This module bundles the seminars at the Chair of Embedded Systems:
Internet of Things (IoT) for Healthcare
Internet of Things (IoT) in Embedded Systems
Approximate Computing
Thermal-aware Embedded Systems
Dependability in Internet of Things (IoT)
Performance Optimization for Multicore Chips
Power Efficient Reliability
Distributed Decision Making
Low Power Design for Embedded Systems
Reconfigurable Embedded Systems
Mixed Criticality Systems
Security in Internet of Things (IoT)
For current information, please refer to the course catalog and the Chair of Embedded Systems homepage at http://ces.itec.kit.edu.
This is identical to the module 'Seminars: Embedded Systems I' and enables participation in a second seminar at the CES Chair.
90 h
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 10/1/2025)
Elective Studies in Informatics (Usage from 10/1/2025) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113892 | Seminar: Exoskeletons & Motion Capture | WT | 3 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
- The students know the state of the art of exoskeletons and assistive robots and current medical applications.
- Students are familiar with the most important methods and devices for measuring human movements with and without robotic mobility assistance systems
- Students are able to independently research, understand, critically evaluate and summarize scientific literature on a given topic (usually in English).
- Students will be able to prepare and deliver a scientific presentation, taking into account the level of knowledge of the other seminar participants, and to answer detailed questions on the topic
- Students will be able to ask questions about scientific presentations of other students and make active contributions to scientific discussions
- Students will be able to use Latex to create a scientific text (in English) incorporating the sources they have read.
This seminar provides an insight into the current state of research and the practical use of exoskeletons in medicine and their potential to improve the quality of life of people with and without physical disabilities. It also gives an overview of different motion capture technologies, which play an important role in exoskeleton research but have many other applications. The proseminar is offered by the research group Optimization and Biomechanics for Human-Centred Robotics at the IAR.
Exoskeletons are used to improve mobility and are worn by people directly on the body to support or completely replace their muscle strength. One class of exoskeletons assists people with mobility impairments to walk, stand and perform other physical activities so that they can regain their independence and participate in the activities of daily living. Other types of exoskeletons are used by healthy people to prevent injuries in difficult working conditions.
The applications of motion capture include many disciplines in which human behavior and movements are of interest, such as sports science, biomechanics, medicine, psychology, etc., but they also play an important role in the creation of animated films and computer games. In robotics, and especially in the development of exoskeletons, they are important for analyzing human-robot interaction and improving robot design and control. Various motion capture technologies exist for recording movement kinematics, ground reaction forces and muscle activity.
The proseminar presentation topics cover the spectrum of different exoskeleton types and motion capture technologies.
Schedule of the proseminar:
In an event in the first week of the semester, the topic of the seminar and the individual lecture topics are presented in detail and distributed to the students according to their preferences. In a second session in week 3, an introduction to academic literature research, writing and presentations will take place. Afterwards, the students carry out a literature search under supervision and prepare a presentation and a corresponding paper. As the scientific literature is mostly in English, the paper should also be written in English. Presentations can be held in either English or German and take place in one or more block courses during the last third of the semester.
Limited number of participants
Estimated effort for this module is 90 hours:
20h – In person events (kickoff meeting, indiidual preparatory meetings, seminar block)
20h – Literature research
20h – Preparation of presentation
30h - Paper writing
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) is helpful
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113115 | Seminar: Explainable Artificial Intelligence | WT | 3 | Lioutikov |
See partial achievements (Teilleistung).
See partial achievements (Teilleistung).
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic.
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic. Students get a deeper understanding of state-of-the art learning algorithms and get to know current research challenges.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Each student will select several related papers in the field of Explainable Artificial Intelligence and study and analyze it in the context of Robot Learning. The organizers will suggest several papers but the students will be encouraged to indentify and research additional relevant papers during the semester. The students will then prepare a presentation and a basic scientific research paper.
It is highly recommended to take this seminar in combination with the “Explainable Robot Intelligence” Research Project (Forschungspraktikum), where the students get the chance to deepen their understanding, implement and evaluate their presented work.
It is only possible to resign within two weeks after assignment of the topic.
Workload = 90 h = 3 ECTS
- Attendance time: 15h
- Self-study: 45h
- Writing a scientific report: 20h
- Preparing a presentation: 10h
We recommend taking this research project after attending the “Explainable Artificial Intelligence” lecture in the summer semester.
We highly recommend to take this seminar in combination with the “Explainable Artificial Intelligence” research project (Forschungspraktikum).
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113398 | Seminar: Exploring Robotics - Insights from Science Fiction, Research and Society | ST | 3 | Bruno |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students gain experience with literature research on a current research topic. They explore, understand and compare different approaches to a selected scientific problem. The students are able to write a summary of their literature research in the form of a scientific publication in English and give a scientific talk on it.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The students choose a topic from the field of robotics (e.g. remote control, behavior-based robotics, human-robot interaction, the “uncanny valley,” natural language understanding, machine learning) and conduct a research on it that, building on literature findings, also includes and addresses the perspectives of society and the general media (as given by science fiction books, movies and games, as well as media and news outlets) and technology assessment (including social/societal expectations and needs, ethical implications, and risks/benefits analyses).
Students work under the guidance of a scientific supervisor. At the end of the semester, they present the results and write an elaboration in English in the form of a scientific publication.
Seminar with 2 SWS, 3 LP.
3 LP corresponds to approx. 90 hours, of which
approx. 45 hours of literature research
approx. 25 hrs. elaboration
approx. 10 hrs. preparation of presentation
approx. 10 hrs. compulsory attendance
Knowledge of the content of modules Robotics I - Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics is helpful.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114604 | Seminar: Failing Forward: Robotics, Critical Reading and the Making of Good Research | WT | 3 | Bruno |
See partial achievements (Teilleistung
See partial achievements (Teilleistung)
The students refine their critical reading skills and gain experience with literature research on a current research topic. They can identify and propose good research practices. The students are able to present their findings and reflections in the form of a scientific publication in English and give a scientific talk on it.
The students choose a topic from the field of human-robot interaction (e.g. about robots in public spaces, or robots and diversity, or companion robots for older adults). They then conduct a literature research under the guidance of a scientific supervisor, aimed at identifying what makes good research and good research reporting on the chosen topic. At the end of the semester, they present the results and write an elaboration in English in the form of a short scientific position paper.
Seminar with 2 SWS, 3 LP.
3 LP corresponds to approx. 90 hours, of which
approx. 45 hours of literature research
approx. 25 hrs. elaboration
approx. 10 hrs. preparation of presentation
approx. 10 hrs. compulsory attendance
Knowledge of the content of modules Robotics I - Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113392 | Seminar: Fine-Grained Complexity Theory & Algorithms | Irreg. | 4 | Künnemann-Goos |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to:
- perform a literature review on the basis of a given topic/scientific paper, to read and understand relevant scientific works in algorithms & complexity theory and to identify the scientific context.
- present a scientific paper and its context. This includes competency in tools and techniques for making the content accessible for a target audience.
- create a written report of their topic in accordance to usual quality standards
for scientific writing
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Selected topics from the field of fine-grained complexity theory & algorithm design. This consists of recent papers on fine-grained hardness assumptions, conditional lower bounds and algorithmic results for important problems from various sub-areas.
Each student will present a topic and summarize it in a scientific report.
4 CP amounts to 120 h, distributed as follows:
- about 10 h attendance in class
- about 40 h literature search and review
- about 40 h preparation of presentation
- about 30 h writing of scientific report
Basic knowledge of theoretical computer science and algorithm design is recommended.
Concurrent or previous attendance of the lecture “Fine-Grained Complexity Theory & Algorithms” is helpful, but not required. This seminar can be attended independently.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113761 | Seminar: Hot Topics in Artificial Intelligence & Security 1 | WT | 4 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know basic concepts artificial intelligence and machine learning in computer security, and are able to understand/interpret results from state-of-the-art research.
• Students know and understand basic concepts of combining artificial intelligence and computer security.
• Students are able independently research topics and methods.
• Students understand limits of current methods and applications
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work
This seminar is concerned with the combination of artificial intelligence, machine learning and computer security in practice. Many tasks in the security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security
solutions (AI for Security). However, also these learning-based systems can be attacked and need to be secured (Security of AI).
This module is part of a seminar series to intensifies the contents of the AISEC lecture. It can be attended individually and in no particular order. The module puts focus on timely topics from recent research and teaches students to work up results from state-of-the-art research. To this end, the they will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.
- 30h Literature research
- 60h Elaboration of the seminar paper
- 20h Preparation of final presentation
- 10h attendance time
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113762 | Seminar: Hot Topics in Artificial Intelligence & Security 2 | ST | 4 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know basic concepts artificial intelligence and machine learning in computer security, and are able to understand/interpret results from state-of-the-art research.
• Students know and understand basic concepts of combining artificial intelligence and computer security.
• Students are able independently research topics and methods.
• Students understand limits of current methods and applications
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work
This seminar is concerned with the combination of artificial intelligence, machine learning and computer security in practice. Many tasks in the security landscape are based on manual labor, such as searching for vulnerabilities or analyzing malware. Here, machine learning can be used to establish a higher degree of automation, providing more "intelligent" security
solutions (AI for Security). However, also these learning-based systems can be attacked and need to be secured (Security of AI).
This module is part of a seminar series to intensifies the contents of the AISEC lecture. It can be attended individually and in no particular order. The module puts focus on timely topics from recent research and teaches students to work up results from state-of-the-art research. To this end, the they will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.
- 30h Literature research
- 60h Elaboration of the seminar paper
- 20h Preparation of final presentation
- 10h attendance time
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101287 | Seminar: Hot Topics in Bioinformatics | ST | 3 | Stamatakis |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Participants will be able to understand, critically evaluate and compare current scientific publications in the field of sequence-based bioinformatics. They are able to present, understand, and critically assess the algorithms and models from current publications orally and in writing at a level that corresponds to the quality of scientific publications and conference presentations. They are able to suggest possible extensions to existing work and assess if the results are reproducible.
The field of Bioinformatics is by now established as an independent application area of computer science. One of the main objectives of classical bioinformatics is to generate biological knowledge (usually from molecular data, e.g., DNA data sets) using appropriate models and algorithms. The so-called molecular data flood, which is being driven by increasingly faster and cheaper methods for extracting DNA, presents bioinformatics with new challenges regarding data storage and processing. These challenges range from discrete algorithms on strings and trees to parallel processing of data and large numerical simulations on supercomputers. The aim of the module is to provide an insight into the many facets of current bioinformatics research.
10 hours of topic selection + 10 hours of attending the seminar lectures + 30 hours of reading and understanding the paper(s) + 10 hours of lecture preparation + 30 hours for writing the report = 90 hours = 3 ECTS
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Telematics Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-109922 | Seminar: Hot Topics in Decentralized Systems | Irreg. | 3 | Hartenstein |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student is familiar with the current state of research in the field of decentralised systems.
The student is able to familiarise him/herself independently with a current research topic and the associated fundamentals by identifying relevant literature and processing it in a structured manner.
The student is able to write a paper according to scientific standards.
The student is able to present and discuss a scientific topic in a colloquium.
The student is able to consider the challenges of a specific technical problem in the context of decentralised systems and transfer existing solution approaches to the given problem and evaluate them with regard to performance and security.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The seminar deals with current work in the field of decentralised systems. Based on current research work, challenges and approaches are identified. Corresponding solutions are analysed and compared. Finally, the reference to related domains is established.
Kick-off events: 4h
Meeting with the supervisor: 4h
Presentation dates: 8h
Literature research: 25h
Writing the paper and preparing the presentation: 50h
Total: 91h = 3 ECTS points
Knowledge of the basics of IT security management for networked systems and the basic security module is helpful.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112917 | Seminar: Hot Topics in Explainable Artificial Intelligence (XAI) | ST | 4 | Wressnegger |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students know concepts of explainable machine learning and are able to understand/interpret results from state-of-the-art research.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
This seminar is concerned with explainable machine learning in computer security. Learning-based systems often are difficult to interpret, and their decisions are opaque to practitioners. This lack of transparency is a considerable problem in computer security, as black-box learning systems are hard to audit and protect from attacks.
The module introduces students to the emerging field of explainable machine learning and teaches them to work up results from recent research. To this end, the students will read up on a sub-field, prepare a seminar report, and present their work at the end of the term to their colleagues.
Topics cover different aspects of the explainability of machine learning methods for the application in computer security in particular.
- 24h literature research
- 48h Elaboration of the seminar paper
- 24h Review of preliminary work by fellow students
- 16h preparation of final presentation
- 8h attendance time
In total 120h
| Coordinators: |
Prof. Dr.-Ing. Klemens Böhm
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
(Usage from 4/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115055 | Seminar: Information Systems | WT+ST | 3 | Böhm |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines when writing their scientific work.
The Chair of Information Management Systems offers at least one seminar per semester on a selected information systems topic (every seminar at the "Chair of Information Management Systems" that is not a proseminar counts as an "Information Systems Seminar").
These are usually current research topics, for example from the fields of databases, data mining or workflow management.
Details are announced each semester (notices and homepage of the Chair of Information Management Systems).
Total = 90h (3 ECTS)
Lectures of the Chair of Information Management Systems that match the topic of the seminar are strongly recommended.
| Coordinators: |
Jun.-Prof. Dr. Jan Stühmer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114237 | Seminar: Interpretability and Causality in Machine Learning | ST | 3 | Stühmer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification target:
Students acquire the foundations of scientific literature research, writing of a scientific report, and presenting their results in front of an audience.
Learning objectives:
Students independently acquire an understanding of their research topic from scientific literature such as conference papers, journal papers and textbooks.
They are able to independently present the content in a concise and understandable way in a written report and in a presentation in front of an audience.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Topic of this Masterseminar are machine learning approaches and deep learning methods for learning of interpretable representations. These methods enable to reconstruct underlying principles from data, for example the reconstruction of generative factors of a dataset.
Starting from these methods for interpretable representations, we will discuss further methods for causal discovery, that enable the inference of causal dependencies in data.
Methods and algorithms covered include for example variational inference, contrastive learning, as well as statistical methods for factor analysis.
There will be a kick-off meeting at the beginning of the semester and 2-3 block seminars towards the end of the term.
Dates for both will still be determined.
The Masterseminar will be held in English language.
90h
Attendance of the lecture "Machine Learning - Fundamentals and Algorithms" is recommended.
| Coordinators: |
TT-Prof. Dr. Frederike Zufall
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Minor Studies: Law
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114094 | Seminar: Law and Legal Studies | WT+ST | 3 | Gil Gasiola, Pathak |
See partial archievements (Teilleistung)
See partial archievements (Teilleistung)
The student
Accompanied by the relevant examiners, the student practises independent scientific work when writing the final seminar papers and presenting them.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines when writing their scientific work.
The module consists of a seminar that is thematically related to law. A list of approved courses will be published on the Internet.
The total workload for this module is approx. 90 hours (3 credits) for attendance time, preparation and follow-up work as well as the course examination.
The actual workload varies depending on the specific seminar chosen and is described for the individual course.
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113519 | Seminar: Machine Learning in Climate and Environmental Sciences | Irreg. | 3 | Nowack |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students will learn to:
• independently discuss current research topics on machine learning in climate and environmental sciences.
• summarize published research in a structured way and explain it in their own words, in group discussions and in the form of a presentation.
• contrast modern problem-solving approaches and methods and propose suitable solutions for a variety of subject-relevant issues.
• Optional: students are invited to develop their own research ideas on the basis of what they have learned and to refine them in discussion with their supervisors. Such ideas could be pursued as a project internship, in the “Practical Research course” or in the form of a master’s thesis.
Machine learning (ML) methods are already ubiquitous in many areas of society and research. This is especially true for climate and environmental sciences, where ML algorithms help e.g. to improve predictions of climate change and weather, or to optimize energy supply systems. In this session, we will discuss cutting-edge publications on ML applications in climate and environmental sciences, as well as the underlying theory behind the classes of algorithms. While organizers will suggest initial papers, students will be encouraged to seek out additional relevant literature throughout the semester.
The seminar will cover both the in-depth study of the climate/environmental sciences topic as well as of the specific machine learning method(s) employed in the literature. It will include two short and one longer final presentation from each student. The first presentation will focus solely on the chosen climate or environmental event or phenomenon, while the second presentation will cover the machine learning methods employed in studying it. Next to suggested reading by the module organizers, students will be encouraged to seek out additional relevant literature throughout the semester.
Towards the end, students will compile their findings into the final presentation accompanied by a scientific report, presenting the results in the form of a lecture.
Total 90 h, consisting of:
Seminar attendance and personal meetings with the supervisors: 10 h
Literature research: 30 h
Writing the seminar paper and preparing the final presentation: 50 h
• An interest in climate and environmental sciences topics is a prerequisite.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
(Usage from 10/1/2026)
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115209 | Seminar: Neural Networks and Artificial Intelligence | WT | 3 | Niehues |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students learn to independently familiarize themselves with topics using academic literature and to prepare them for presentations.
Through the other presentations, students gain in-depth knowledge in specific areas of neural networks.
By evaluating their peers’ presentations, students improve their social skills.
Students are familiar with the DFG Code of Conduct “Guidelines for Ensuring Good Scientific Practice” and successfully apply these guidelines when writing their academic papers.
Even the fastest computers are no match for the human brain when it comes to many tasks that we take for granted. Neural networks attempt to simulate the brain’s parallel and distributed architecture in order to better master these capabilities through learning processes. In this context, the course covers neural approaches in image and speech recognition, robotics, and other fields.
Students independently research individual topics using the provided literature and present their findings in the form of a slide-based presentation to the other seminar participants.
Approx. 6 in-person sessions = 12 hours; seminar
paper and presentation = 78 hours;
total = 90 hours
Advanced Artificial Intelligence
| Coordinators: |
Prof. Dr.-Ing. Frank Bellosa
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114230 | Seminar: Operating Systems | Irreg. | 3 | Bellosa |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students analyse and present scientific work in the field of operating systems.
In addition to techniques of scientific work, key qualifications are also taught in an integrative manner by attending the seminars.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines when writing their scientific work.
The seminar is dedicated to a current area of operating system research.
30 h = 2 SWS * 15 attendance
30 h preparation
10 h Presentation
20 h elaboration
90 h = 3 ECTS
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111200 | Seminar: Post-Quantum Cryptography | Irreg. | 3 | Müller-Quade |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The Seminar deals with the foundations of post-quantum cryptography and quantum hard problems.
First, the mathematical basics describing several quantum-hard problems are introduced in introductionary lectures. Subsequently, different post-quantum cryptosytems and common cryptographic notions will be introduced. Furthermore the seminar covers related topics, such as provability in the event of quantum adversaries.
Attendance time in seminar: 15 h
Writing the paper: 30 h
Designing and preparing the presentation: 45 h
Basic knowledge of IT-Security and cryptography are recommended.
| Coordinators: |
Prof. Dr. Henning Meyerhenke
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114297 | Seminar: Practical Graph Algorithms | Irreg. | 4 | Meyerhenke |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students are able to:
- perform a literature review on the basis of a given topic/scientific paper, to read and understand relevant scientific works in algorithm engineering for graph problem and to identify the scientific context.
- present a scientific paper and its context. This includes competency in tools and techniques for making the content accessible for a target audience.
- create a written report of their topic in accordance to usual quality standards for scientific writing
- critically assess the work of other participants and make constructive suggestions for improvement.
- Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
This seminar covers various topics from the field of practical graph algorithms such as small subgraph detection, graph robustness, centrality computations, and related ones. The exact focus of the seminar for the current semester will be announced in advance on the website of Prof. Meyerhenke’s chair. Participants in the seminar carry out their own literature research, present their results to their fellow students and prepare a paper.
The seminar will be held in several blocks, partially online, partially on-site. The exact formalities will be announced at an online kick-off event at the beginning of the semester, which will also be announced on the course website mentioned above.
4 LP corresponds to approx. 120 working hours, of which
- 15h seminar attendance
- 35h Literature research, assessment and evaluation of relevant literature
- 35h preparation of own presentation
- 35h preparation of the scientific report
Knowledge of algorithms, in particular graph algorithms, is a clear advantage. Exemplary lectures are Algorithms I and Algorithms II.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114236 | Seminar: Privacy and Security | WT | 4 | Strufe |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student is able to
- conduct a literature search based on a given topic, identify and evaluate the relevant literature;
- independently compile research results from IT security and technical data protection;
- analyze and discuss scientific studies and place them in their context;
- conduct their own classifications and evaluations of scientific studies, report on them in writing and present the results in a short scientific presentation.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The seminar deals with current topics from the research field of data protection and security.
These include, for example:
- Privacy attacks on communication
- Network security
- Anonymized online services
- Evaluation of the anonymity of online services
- Anonymized publication of data (differential privacy, k-anonymity)
- Transparency/awareness-enhancing systems
- Behavioral analysis of media use
- Biometric authentication
Seminar attendance time: 10h
Researching and writing a paper: 75h
Reviewing and commenting on the preliminary papers of fellow students: 5h
Preparing the presentation: 30h
Fundamentals of IT security, computer networks and distributed systems are required
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-106604 | Seminar: Proofs from THE BOOK | Irreg. | 3 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students learn to follow and understand complex mathematical proofs on their own. They learn to represent these proofs in an appealing manner and present the proofs to the other participants using a blackboard.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
According to the Hungarian mathematician Paul Erdős, God is keeping a book – the BOOK – under wraps that contains the most elegant mathematical proofs. Erdős’ loftiest goal was to find such proofs from the BOOK.
After Erdős’ death in 1996, Martin Aigner and Günter Ziegler published the book “Proofs from THE BOOK” in 1998. The book has also been published in German with the title “Das BUCH der Beweise”. In Aigner and Ziegler’s collection, there are some 40 of the most elegant proofs which are handled as candidates for BOOK-proofs.
In this seminar, the participants will present and discuss proofs from “Proofs from THE BOOK” and other well known and well studied proofs in the area of mathematics and informatics.
Seminar with 2 SWS, 3 LP
3 LP correspond to about 90h of work, split into
about 20h attendance
about 60h preparation for seminar
about 10h follow-up
The German version “Das Buch der Beweise” is available online at the KIT library within the KIT network. The English version “Proofs from THE BOOK” is available as a physical copy at the KIT library. We recommend having a look inside either version before registering for this seminar.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-110904 | Seminar: Quantum Information Theory | Irreg. | 3 | Müller-Quade |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The student…
• understands the basics of Quantum Information Theory.
• understands formalizing quantum states via state vectors and is able to autonomously use the state vector formalism to design and analyze quantum algorithms.
• knows and understands the quantum gates introduced in the seminar.
• knows the visual quantum circuit tool “Quirk” and is able to autonomously apply it to design and analyze quantum algorithms.
• knows and understands the quantum problems and algorithms discussed in the seminar and is able to explain them and relate them to one another.
• knows and understands the impact quantum algorithms have on classic cryptography.
• knows and understands the basics of and presented protocols for quantum key distribution.
• is able to autonomously apply the techniques presented in the seminar, e.g. to prove correctness of simple quantum algorithms.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
• Basics of Quantum Information Theory
• Formalism for dealing with quantum systems
• “Quirk”
• Important quantum problems and algorithms
• Quantum key distribution
• Quantum walks
Seminar attendance time: 18h
Preparation and follow-up work: 12h
Preparation of a presentation: 30h
Preparation of a written examination: 30h
Students should be familiar with the contents of the module "Linear Algebra 1 and 2", as well as the basics of IT security.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Algorithm Engineering Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114201 | Seminar: Recent Highlights in Algorithms | Irreg. | 4 | Bläsius, Sanders, Ueckerdt |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can,
- carry out a literature search based on a given topic, identify, locate, evaluate and finally analyse the relevant literature.
- prepare presentations in a scientific context. To this end, students master techniques that enable them to prepare and present the content to be presented in a manner suitable for an audience.
- prepare their written seminar paper (as required later for further academic work) in accordance with the requirements and quality standards of academic writing, taking into account the format requirements specified by academic publishers for the publication of documents.
- critically assess the work of other participants and make constructive suggestions for improvement.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
The seminars offered as part of this seminar module deal with current topics in algorithm technology and explore them in depth. As a rule, the prerequisite for passing the module is the preparation of a written paper of max. 15 pages and an oral presentation of at least 45 minutes.
Seminar with 2SWS, 4LP
4 LP corresponds to approx. 120 working hours, of which
approx. 10h seminar attendance
approx. 40 hours of literature research, assessment and evaluation of relevant literature
approx. 30h preparation of own presentation
approx. 30 hours writing the paper
approx. 10h Reading two papers and formulating constructive criticism and suggestions for improvement in writing
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 7/29/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 7/29/2026) Elective Studies in Informatics (Usage from 7/29/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115349 | Seminar: Robot Reinforcement Learning | Irreg. | 3 | Neumann |
See partial achievement (Teilleistung).
See partial achievement (Teilleistung).
Students are able to independently understand a complex research topic, present the content in a concise and understandable way and prepare a scientific report summarizing the topic. Students get a deeper understanding of state-of-the art RL algorithms and get to know current research challenges.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Reinforcement Learning is a popular machine learning method where an artificial agent has to learn how to act optimally in an unknown environment by trial and error. In this seminar, we will focus on recent developments in RL for robotics, i.e., RL for continuous state and action spaces.
The students can choose from different topics from the area of reinforcement learning (RL) for robotics, including deep reinforcement learning, model-free RL, actor-critic methods, model-based RL, meta learning, hierarchical reinforcement learning and robot applications of RL. Each topic consists of several research papers for which the students have to prepare a presentation as well as a report in form of a scientific research paper.
Workload = 90 hours = 3 ECTS
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-111501 | Seminar: Secure Multiparty Computation | Irreg. | 3 | Müller-Quade |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to familiarize themselves thoroughly with scientific papers, to present them to other students and to deal with questions on their topic in a subsequent discussion round.
Students will be able to differentiate between different protocols for secure multiparty computation and weigh up their advantages and weaknesses.
Students will be able to present academic publications from the research field of secure multiparty computation in a suitable manner, place them in the historical context of the research field and critically examine the results and findings presented.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and apply these guidelines successfully in the preparation of their academic work.
In the setting of secure multiparty computation, two or more parties with private inputs wish to compute some joint function of their inputs. The security requirements of such a computation are privacy (meaning that the parties learn the output and nothing more), correctness (meaning that the output is correctly distributed), independence of inputs, and more. Due to its generality, secure computation is a central tool in cryptography.
In this seminar, we examine modern protocols for secure multiparty computation of arbitrary functions.
Attendance time in seminar: 15 h
Meeting with supervisors: 5 h
Independent work in relation to the individual seminar topic: 70 h
Knowledge of the content of the lecture Cryptographic Protocols is assumed.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114260 | Seminar: Software Architecture, Security and Privacy | Irreg. | 4 | Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can,
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and apply these guidelines successfully in the preparation of their scientific work.
Anyone who processes personal data automatically must effectively protect this data from unauthorized access in order to act in accordance with data protection laws, but also to prevent damage to reputation and trustworthiness should data protection violations become public. Protecting personal data from unauthorized access and complying with other data protection obligations is therefore actually one of the most important goals in software design and operation.
However, looking at data protection in isolation does not do justice to reality. If an attacker gains access to personal data, voluntary commitments and internal data protection regulations no longer apply. In case of doubt, the operator of the software is liable for severe fines. Effective security precautions are therefore indispensable as a mainstay for protecting personal data.
Security-critical vulnerabilities must be identified at an early stage, ideally before the vulnerability is introduced. Such quality assessments are performed by software architecture-based analyses. How security can be described and analyzed at the software architecture level is the subject of ongoing research, as is the question of whether - and how - security can be expressed in figures.
In this seminar, students deal with these questions and the state of research at the interface between data protection, security and software architecture. Possible topics are located in one or more of these areas.
25 working hours for literature research
55 working hours for writing the thesis and preparing peer reviews
20 working hours for preparing the final presentation
20 working hours for the final block event and meeting with the supervisor.
This results in a total of 120 working hours
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Human-centred Machine Intelligence
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114208 | Seminar: Speech-to-Speech Translation | ST | 3 | Niehues |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn to familiarise themselves independently with topics based on academic literature and prepare them for presentations.
From the other presentations, students gain in-depth knowledge in sub-areas of language-to-language translation
By evaluating the presentations of their fellow students, students improve their social skills.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and successfully apply these guidelines in the preparation of their scientific work.
Speech-to-speech translation is a popular application that combines automatic speech recognition and machine translation. However, a user-friendly combination requires more than just a linear connection of the individual techniques.
In this seminar, students work independently on individual topics from the fields of automatic speech recognition, machine translation and their combination into speech-to-speech translation systems using the literature provided and present the summarised findings to the other participants in the seminar in the form of a slide-based presentation.
90 h
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 10/1/2025)
Area of Specialization: Software Engineering and Compiler Construction (Usage from 10/1/2025) Elective Studies in Informatics (Usage from 10/1/2025) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114955 | Seminar: Type Systems and Static Program Analysis | WT | 4 | Erdweg |
See partial achievements (Teilleistung)
None.
Students can:
• carry out a literature search based on a given topic. They can identify, locate, evaluate and comprehend the relevant literature.
• prepare and deliver a scientific presentation on a given topic, using adequate presentation techniques.
• write a seminar paper that meets the requirements and quality standards of academic writing.
• participate in academic discussions with their peers.
Students are familiar with the DFG Code of Conduct "Guidelines for Safeguarding Good Scientific Practice" and apply these guidelines successfully in the preparation of their scientific work.
Type systems and static program analyses predict the behavior of programs through source-code analysis, but without executing the program. They are fundamental for isolating ill-defined program behavior (e.g., from type errors), but also can provide guarantees (e.g., memory safety). Topics covered in this seminar include the design, definition, formalization, and application of type systems and static program analyses, including type inference, dependent typing, control flow, data flow, abstract interpretation, soundness, and precision.
10 working hours for independent study of the seminar topic
25 working hours for literature research
20 working hours for preparing the presentation
55 working hours for writing the thesis and preparing peer reviews
10 working hours for the final block event and meetings with the supervisor
This results in a total of 120 working hours
Students should be familiar with the foundational knowledge about type systems (e.g., typing rules, type checking, polymorphism) or static program analysis (e.g., data flow, abstract interpretation).
| Coordinators: |
Prof. Dr. Gerhard Satzger
Prof. Dr. Orestis Terzidis
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
Minor Studies: Economics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-WIWI-102849 | Service Design Thinking | Irreg. | 9 | Satzger, Terzidis |
The assessment is carried out as a general exam (according to Section 4(2), 3 of the examination regulation). The overall grade of the module is the grade of the examination (according to Section 4(2), 3 of the examination regulation).
None
Students
Course phases (roughly 4 weeks each):
Design Space Exploration:
Critical Function Prototype:
Dark Horse Prototype:
Funky Prototype:
Functional Prototype:
Final Prototype:
Due to practical project work as a component of the program, access is limited. The module (as well as the module component) spans two semesters. It starts in September every year and runs until end of June in the subsequent year. Entering the program is only possible at its beginning - after prior application in May/June. For more information on the application process and the program itself are provided in the module component description and the program's website (https://sdtkarlsruhe.de/). Furthermore, the lecturers provide an information event for applicants every year in May.
The workload for this module is approx. 2 days per week over a period of 9 months. The workload for this practical module is therefore comparatively high. The reason for this is that the participants work in international teams with students from other universities and partner organizations and solve real innovation challenges.
The workload of approx. 270 hours is spread over approx. 105 hours (3.5 CP) in the first semester and 165 hours (5.5 CP) in the second semester.
This course is held in English – proficiency in writing and communication is required.
Our past students recommend to take this course at the beginning of the masters program.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Elective Studies in Informatics
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-113123 | Simulation and Optimization in Robotics and Biomechanics | WT | 6 | Mombaur |
| T-INFO-114838 | Simulation and Optimization in Robotics and Biomechanics - Pass | WT | 0 | Mombaur |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The students
• can explain advanced principles of modeling, optimization and control of dynamic processes, in particular mechanical systems and can apply them
• can model, classify and analyze complex motions in robotics or biomechanics, and investigate specific properties such as stability.
• can apply nonlinear optimization and optimal control methods and can compare and evaluate different mathematical approaches.
• know how to use software tools based on C++ and Lua for modeling, simulation, optimization and visualization of humanoid and robotic systems
are capable of solving optimal control problems numerically and to evaluate the quality of the solution.
The goal of this course is to give a practical introduction into simulation and optimization of motions in robotics and biomechanics. Simulation and optimization play an important role in generating and controlling motions in complex robotics systems and in predicting and analyzing motions of humans. Theory and methods will be covered, but the focus is on the use software tools for modeling, simulation, optimization and visualization of multibody systems. Topics covered include:
• Dynamic process modeling
• Transforming real world problems into mathematical models
• Modeling of complex robotics and biomechanics systems (e.g. humanoids), based on previous modeling knowledge
• Common template models for bipedal walking and running in robotics and biomechanics
• Simulation of mechanical / robotics systems (Integrators and Initial value problems)
• Boundary value problems
• Nonlinear optimization problems
• Optimal control problems
• Direct and indirect methods for optimal control problems, focus on direct methods, especially direct multiple shooting
• Stability of dynamical systems, stability in biomechanics and robotics
Limitation to 30 participants
Estimated effort for this module is 180 hours:
60h - Lecture and exercises (2+2 SWS)
80h - Independent work (repetition of lecture contents, preparation of assignments
40h - Exam preparation
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114261 | Software Architecture and Quality | WT | 3 | Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can explain the role of components and explicit software architecture descriptions for engineering software development.
They can also explain the basic concepts of component-based software development.
Students are familiar with advanced concepts of view-based metamodeling and can apply these to the scenarios of the software development domain.
In addition, they can use procedures for the documentation, evaluation and reuse of software architectures, such as architecture patterns or architecture styles.
Furthermore, they can differentiate between and use different software development processes.
Students can design models for software quality characteristics such as performance.
The effects of architecture design decisions on software quality characteristics such as performance can also be analyzed.
In many software development projects, the software architecture is the main determining factor for software quality. Runtime properties such as performance or reliability, as well as maintainability, essentially depend on the architecture of a software system.
In the lecture, students learn about and apply modern approaches to software architecture modeling and analysis, which can be used to predict the quality characteristics of the system at design time. The lecture thus lays the scientific foundations for software design as an engineering discipline, as the methods learned enable an understanding of the effects of architectural design decisions on software quality. In particular, software qualities such as performance, reliability and maintainability are discussed.
In connection with software architecture, software components are also introduced as "software building blocks". In particular, techniques for the reuse of architectural knowledge such as patterns, styles and reference architectures and product lines are discussed.
The lecture deals with the Palladio component model as a description language for software components and architectures.
Using the Palladio component model, role models for the design and development of component-based software are presented in addition to quality prediction.
Its use is demonstrated using industry-related case studies and techniques for evaluating the quality of your software architecture are illustrated.
The lecture covers technologies such as MOF, OCL and architecture-centered, model-driven software development (AC-MDSD). Modern middleware from practice such as Java EE / EJB is also presented.
(2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
Prof. Dr. Raffaela Mirandola
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114259 | Software Engineering II | ST | 6 | Koziolek, Mirandola, Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Software processes: Students understand evolutionary and incremental development and can describe the advantages over the sequential approach. They can describe the phases and disciplines of the unified process.
Requirements engineering: Students can describe the terms of requirements engineering and name activities in the requirements engineering process. They can classify and assess requirements according to the facets of type and representation. They can apply basic guidelines for specifying natural language requirements and describe prioritization procedures for requirements. Describe the purpose and elements of use case models. You can classify use cases according to their granularity and objectives. You can create use case diagrams and use cases. They can derive system sequence diagrams and operation contracts from use cases and can describe their role in the software development process.
Software architecture: Students can reproduce and explain the definition of software architecture and software components. They can explain the difference between software architecture and software architecture documentation. They can describe the advantages of explicit architecture and the factors influencing architecture decisions. You can assign design decisions and elements to the layers of an architecture. You will be able to describe what component models define. They can describe the components of the Palladio component model and discuss some of the design decisions made.
Enterprise Software Patterns: Students can characterize enterprise applications and decide for a described application which properties it fulfills. They know patterns for structuring domain logic, architectural patterns for data access and object-relational structure patterns. They can select a suitable pattern for a design problem and justify the selection based on the advantages and disadvantages of the patterns.
Software design: Students can assign the responsibilities resulting from system operations to classes or objects in object-oriented design using the GRASP patterns and thus design object-oriented software.
Software quality: Students know the principles for readable program code, can identify violations of these principles and develop proposals for solutions.
Model-driven software development: Students can describe the goals and the idealized division of labor of model-driven software development (MDSD) and reproduce and explain the definitions for model and metamodel. They can discuss the goals of modeling. You will be able to describe the model-driven architecture and express constraints in the Object Constraint Language. You can express simple transformation fragments of model-to-text transformations in a template language. You can weigh up the advantages and disadvantages of MDSD.
Embedded systems: Students will be able to explain the principle of a real-time system and why they are usually implemented as parallel processes. They can describe a rough design process for real-time systems. They can describe the role of a real-time operating system. They can distinguish between different classes of real-time systems.
Reliability: Students can describe the various dimensions of reliability and categorize a given requirement. They can illustrate that unit tests are not sufficient to evaluate software reliability and can describe how usage profile and realistic error data have an influence.
Domain-driven design (DDD): Students are familiar with the design metaphor of ubiquitous language, Closed Contexts, and Strategic Design. They can describe a domain using the DDD concepts, entity, value objects, services, and improve the resulting domain model using the patterns of aggregates, factories, and depots. They know the different types of interactions between Closed Contexts and can apply them.
Security (in the sense of security): Students can describe the basic ideas and challenges of security assessment. They can recognize common security problems and propose solutions.
Requirements engineering, software development processes, software quality, software architectures, MDD, Enterprise Software Patterns software maintainability, software security, dependability, embedded software, middleware, domain-driven design
The Software Engineering II module is a basic module.
Preparation and follow-up time 1.5 h / 1 SWS
Total workload:
(4 SWS + 1.5 x 4 SWS) x 15 + 30 h exam preparation = 180 h = 6 ECTS
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114234 | Software Product Line Engineering | ST | 3 | Schaefer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students understand the essential concepts (such as modularity, variation point, feature model, feature mapping, configuration, product generator, and product) and techniques (such as feature-oriented domain analysis, variant extraction, delta modelling, variant space analyses, product generation, testing of software product lines) of the development of software product lines, their relationships and their assignment to problem and solution spaces. They are able to understand and apply the different methods for designing software product lines, such as feature-orientated domain analysis or variant extraction. Students are familiar with various product generation strategies and know their advantages and disadvantages in practical use. Students are familiar with techniques for the maintenance of software product lines, such as variant space analysis, the generation of product samples and the testing of software product lines, and are able to apply these. In addition, students are familiar with current results and issues from the research field of software product lines and understand their significance, e.g. results from the field of language product lines.
Learning objectives: Students are able to independently design, implement and maintain a software product line. Students can apply feature-orientated domain analysis to a given domain and design a software product line based on a domain description and implement it in practice with tool support. Students can use variant extraction independently and with tool support to design a software product line from a series of product variants of a software system and implement it by refactoring. Students can select a suitable product generation strategy for a given domain and implement it with tool support. Students can analyse and improve the variant space of a given software product line. Students know different techniques to maintain a software product line and can analyse the variant space, generate product samples and develop tests for a given software product line.
This module teaches students the procedures and techniques for the development and maintenance of multi-variant software systems using software product lines. The lecture will provide an overview of the basic goals, processes, concepts and techniques in the development and maintenance of software product lines. It is subdivided into the subject areas of the problem space and the solution space. In the first topic area, topics such as feature-oriented domain analysis, feature models and analyses of the variant space are dealt with, whereas in the second topic area, different techniques for product generation and testing of product lines are discussed and demonstrated in practice.
In addition, current results and questions from software product line research are presented and discussed.
(2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h
Basic knowledge from the lectures Software Engineering II [T-INFO-101370] and Formal Systems [T-INFO-101336] is helpful.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Cryptography and Security
Area of Specialization: Software Engineering and Compiler Construction Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-112862 | Software Security Engineering | ST | 3 | Gerking, Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Qualification target: Participants will be able to apply measures to detect or avoid vulnerabilities in different development phases.
Learning objectives:
The course deals with the engineering of cyber security along the development cycle of software systems. This includes constructive and analytical development measures to achieve protection goals through systematic prevention and detection of vulnerabilities. The course familiarizes participants with the adoption and implementation of security measures in various development phases. Relevant fundamentals from the field of formal security models are introduced.
(2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h
Knowledge of Software Engineering I and Software Engineering II is recommended.
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114263 | Softwaretest and Quality Management (SQM) | WT | 5 | Schaefer |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
After completing the module, participants will be familiar with the basic principles of software testing. They will be able to apply the general testing process and master the activities and techniques to support it. Participants will be able to
specify test cases in all phases of the software life cycle. They know test procedures and methods with which they can
prepare and carry out software tests efficiently. They are familiar with common methods of
test management methods and test tools for automating test activities.
1. Basics (introduction, definition of terms, principles of software testing, fundamental test process, psychology of testing)
2. Testing in the software life cycle (general V-model, component test, integration test, system test, acceptance test,
testing of new product versions, overview of test types)
3. Static testing (structured group tests, static analyses, metrics)
4. Dynamic testing (black-box procedure, white-box procedure, experience-based test case determination)
5. Test management (test organisation and planning, economic aspects, test strategy, management of test work, error management, requirements for configuration management).
6. Testing tools (types, selection, introduction)
7. Modern test procedures (model-based testing, regression testing, testing of variant-rich systems)
8. Debugging
At the end of the course there is also the opportunity to be certified as an "ISTQB - Certified Tester - Foundation Level". A date and the modalities for the exam will be agreed on in the lecture.
150h
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Software Engineering and Compiler Construction
(Usage from 1/16/2026)
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101256 | Software-Evolution | WT | 3 | Reussner |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students learn about the particular challenges of long-lived software systems and the possibilities of influencing the future development of a software system through targeted software evolution. Students will learn which tools and concepts they can use in the context of software evolution and which factors have an impact on the software development process. In addition to the theoretical basics, students will gain insight into practical examples and suitable tools that simplify the handling of software evolution. A cross-section of implementation aspects, techniques, management and concepts will be conveyed to the participants of the lecture. Students are enabled to analyze, evaluate and improve software systems.
The lecture Software Evolution covers: Software development processes, special features of long-lived software systems, evolution scenarios for software systems, software architecture development, software refurbishment, implementation techniques, architecture patterns, traceability, software evaluation methods, maintainability analyses and tools to support software evolution.
(2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 10/1/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115261 | Stochastic Information Processing | WT | 6 | Hanebeck |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Qualification objective: Students can describe a given nonlinear dynamic model probabilistically and set up the equations for Bayesian inference. If no analytical solution exists, they can estimate the strength of the nonlinearity and select and implement a suitable practical filter for real-time state estimation.
Learning objective: Students are familiar with dynamic state models and methods for recursively estimating the state. The advantages and disadvantages of the various practical filters can be assessed in a problem-oriented manner.
To deal with complex dynamic systems, as they occur for example in robotics, one typically requires both system models and the temporal evolution of the system states. For both system identification and state reconstruction, however, usually only noisy data are available.
For continuous state spaces, an exact calculation of the probability densities is only possible in a few special cases. In practice, general nonlinear systems are often traced back to these special cases by simplifying assumptions. One extreme is linearization with subsequent application of linear estimation theory. However, this often leads to unsatisfactory results and requires additional heuristic measures. At the other extreme are numerical approximation methods, which only evaluate the desired probability densities at discrete points in the state space. Although the basic principle of these methods is usually quite simple, their practical implementation is often difficult and, in particular for higher-dimensional systems, computationally demanding.
As a middle ground, analytical nonlinear estimation methods would therefore often be desirable. In this lecture the main difficulties in the development of such estimation methods are outlined and corresponding solution modules are presented. Based on these building blocks, some analytical estimation methods are discussed in detail as examples, which are very suitable for practical implementation and offer a good compromise between computing effort and performance. Useful applications of these estimation methods are also discussed. Both known methods and the results of current research are presented.
As a theoretical foundation lecture, "Stochastic Information Processing" is an ideal introduction to the ISAS lectures. Conversely, prior knowledge of "Localization of Mobile Agents" (LMA) [course no. 24613] may nevertheless be helpful depending on the type of learner - more concrete applications are highlighted there. In all of our lectures, the content is always derived and explained in detail from the beginning; it is therefore possible to start with SI or LMA.
[1.5 h lecture + 1.5 h exercise (3 SWS)] x 15
+ [4.5 h follow-up lecture + 3.5 h preparation exercise] x 15
+ 15 h exam preparation
= 180 h ≙ 6 ECTS
Knowledge of the basics of probability theory is helpful.
| Coordinators: |
Prof. Dr.-Ing. Sören Hohmann
Prof. Dr. Werner Nahm
Prof. Dr.-Ing. Eric Sax
Prof. Dr. Wilhelm Stork
Prof. Dr. Orestis Terzidis
Prof. Dr.-Ing. Thomas Zwick
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-110291 | Innovation Lab | WT+ST | 9 | Hohmann, Nahm, Sax, Stork, Zwick |
| T-WIWI-102864 | Entrepreneurship | WT+ST | 3 | Terzidis |
| T-WIWI-110166 | SIL Entrepreneurship Project | WT | 3 | Terzidis |
This module consists of an 60-minute written exam on the contents of the Entrepreneurship lectures, as well as 5 other types of exams on the contents of the seminar Entrepreneurship and Innovation Lab in the form of term papers and presentations. All exams results are graded. Details are given in the module components and will be given during the course.
none
Personal competence
Social competence
Innovation and entrepreneurship competence
Systemic technical competence
This module strives to combine technical, social and personal competences from the technical and entrepreneurial domain. The objective is to prepare students as best as possible for entrepreneurial activity within or outside of an established organization. Our teaching methods are research-based with a practical orientation.
The lecture Entrepreneurship as the essential component offers the theoretical basis and provides insight in important theoretical concepts and empirical evidence. Currently released case studies and practical experiences of successful founders support the theoretical and empirical content. In order to run a company for the long term additional knowledge is important. That’s why the lecture also teaches basic principles for opportunity recognition, business modeling, an introduction to entrepreneurial marketing and leadership. Customer-based design methods from the lean startup approach as well as methods of technology-centered innovation are presented. Future founders have to be able to develop and handle resources such as financial and human capital, infrastructure and intellectual property. Further aspects tackle the establishment of an organization and funding of the own project.
The knowledge taught in the lecture Entrepreneurship will be applied in an application-oriented seminar and the labs. Hence we use an action learning approach to extend the taught knowledge by practical skills and reflection capabilities. In an team of five, the students will experience their way from the ideation process to the final pitch in front of investors.
The students are able to choose between the following options concerning the labs:
The module also presents methods of agile system development (Scrum) along with associated validation methods as well as methods for functional prototyping. Gate plans are used within the module to determine the progress of the project. Methods for single person work and teamwork are presented and applied. Additionally group-specific knowledge of the different roles of team members, solutions to conflict situations and interdisciplinary teams are presented.
The module grade consists of the written exam of the Lecture Entrepreneurship (40%), of the submissions and presentation of the Innovation Lab (40%) and of the submissions and presentation of the SIL Entrepreneurship Project (20%).
An application is required to participate in this module. Information about the application: www.kit-student-innovation-lab.de.
Lecture Entrepreneurship: 32h attendance time, 48h preparation and follow-up time, 10h preparation time for assessment
Seminar Entrepreneurship: 34h attendance time, 3h preparation and follow-up time, 53h preparation time for assessment.
Innovation Lab: 8h attendance time, 213h preparation and follow-up time, 49h preparation time for assessment.
This results in a total of 450 hours and a total of 15 LPs for both semesters (15*30/2 = 225).
It is recommended to attend the lecture Entrepreneurship at the same time as the seminar Entrepreneurship Project and the Innovation Lab in the winter semester.
Related courses:
Lecture Entrepreneurship
Seminar Entrepreneurship Project
Innovation Labs
Please note that the courses must be booked in parallel.
Related exams:
Written exams covering the content of lecture Entrepreneurship
Presentation of the Value Profile (seminar Entrepreneurship)
Submission of the Business Plan (seminar Entrepreneurship)
Submission of a Technical Report with requirements list and system architecture (Innovation Lab)
Submission of the reflection of the Gate Plans (Innovation Lab)
Presentation of the High-fidelity (Innovation Lab)
| Coordinators: |
Prof. Dr.-Ing. Eric Sax
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-100675 | Systems and Software Engineering | WT | 6 | Sax |
Written exam of 90 minutes.
Students are given the opportunity to earn a grade bonus through separate task assignments. If the grade of the written exam is between 4.0 and 1.3, the bonus improves the grade by a maximum of one grade level (0.3 or 0.4). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. Bonus points do not expire and remain valid for exams taken at a later date.
none
• Students are able to analyse and explain the functional principles and applications of embedded systems.
• Students are able to evaluate and apply maturity models as well as Software Development Life Cycle models including the waterfall model, V-model, prototyping model, agile models, and DevOps.
• Students are able to apply various creativity techniques to develop innovative solutions to problems. They will be able to derive and analyse requirements.
• Students are familiar with diagram formats software modelling languages; they can evaluate and create these based on problem descriptions of an application area. They will be able to create and evaluate functional, data-oriented, algorithmic, state-oriented, and object-oriented views.
• Students are able to understand and apply various aspects of the realization of embedded systems. They will be able to consider implementation alternatives: hardware, co-design and scheduling aspects.
• Students are familiar with the various testing phases in a project and can explain them. They can assess the reliability of a system and understand the concept of functional safety.
The focus of the course is on processes and methods for the design of systems composed of electrical, electronic and electronically programmable systems that contain software, hardware and mechanical components. The desired competencies of the course include the knowledge and goal-oriented use of modeling techniques, design processes, description and representation tools as well as specification languages that correspond to the current state of the art.
The grade is determined by the written exam and the bonus points.
In total: 180h
Knowledge in Digital Technology and Information and Automation Technology (e.g. module M-ETIT-102102 and M-ETIT-106336)
| Coordinators: |
Prof. Dr.-Ing. Jürgen Bortolazzi
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
Minor Studies: Electrical Engineering
|
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-ETIT-100677 | Systems Engineering for Automotive Electronics | ST | 4 | Bortolazzi |
The type and method (written or oral examination) of the assessment will be announced at the beginning of the course.
The examination will take place without any aids.
none
Students are familiar with the systematic development process of electrical and electronic systems and architectures in the field of vehicle technology and the automotive industry. They are able to apply the tools supporting systematic development and to describe electrical and electronic architectures based on models. You will be able to analyze and evaluate systems in the domains of functional and physical modeling.
The lecture provides knowledge of methods, techniques and procedures used in the development phases of electrical and electronic systems for vehicles.
The module grade is the grade of the written or oral examination. Attendance of the laboratory/exercise for the lecture is a prerequisite for admission to the examination.
The type of assessment (written or oral examination) will be announced at the beginning of the course.
The lecture will be examined in writing on the main date; for the follow-up date, the examination may also be oral.
The examination takes place without aids.
Attendance of the laboratory/exercise for the lecture is a prerequisite for admission to the examination.
1. attendance time in lecture and exercise: 60 h
2. preparation/follow-up of the same: 30 h
3. exam preparation: 30 h
It is recommended to attend the lecture SE (23611)
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
(Usage until 3/31/2027)
Elective Studies in Informatics (Usage until 3/31/2027) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114269 | Telematics | WT | 6 | Zitterbart |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students
Students master the basic protocol mechanisms for establishing reliable end-to-end communication. Students have detailed knowledge of the mechanisms used in TCP for congestion and flow control and can discuss the issue of fairness with multiple parallel transport streams. Students can analytically determine the performance of transport protocols and know methods that fulfill special requirements of TCP, such as high data rates and short latencies. Students are familiar with current topics such as problems introduced by utilization of middle boxes in the Internet, the use of TCP in data centers and multipath TCP. Students can use transport protocols in practice.
Students know the functions of routers in the Internet and can reproduce and apply common routing algorithms. Students can reproduce the architecture of a router and know different approaches to buffer placement as well as their advantages and disadvantages.
Students understand the distinction of routing protocols into interior and exterior gateway protocols and have detailed knowledge of the functionality and properties of common protocols such as RIP, OSPF and BGP. The students are familiar with current topics such as SDN.
Students know the function of media allocation and can classify and analytically evaluate media allocation processes. Students have in-depth knowledge of Ethernet and are familiar with various Ethernet forms and their differences, especially current developments such as real-time Ethernet and data center Ethernet. Students can reproduce and apply the spanning tree protocol.
Students can reproduce the technical characteristics of DSL. Students are familiar with the concept of label switching and can compare existing approaches such as MPLS.
Offered for the last time in WS 26/27!!!!!
Lecture with 3 SWS plus follow-up/exam preparation, 6 CP.
6 CP corresponds to approx. 180 working hours, of which
approx. 60 hours lecture attendance
approx. 60 hours preparation/follow-up work
approx. 60 hours exam preparation
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-101388 | Testing Digital Systems I | Irreg. | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The course provides the basic techniques for testing digital circuits
Testing of digital circuits plays a critical role during the design and manufacturing cycles. It also ensures the quality of parts shipped to the customers. Test generation and design for testability are integral parts of automated design flow of all electronics products. The objective of this course is to provide the foundations for developing test methods for digital systems and provides the techniques necessary to practice design for testability.
This course encompasses the theoretical and practical aspects of digital systems testing and the design of easily testable circuits. Topics include Introduction to Testing (testing definition, types of test, automatic test equipment, test economics, and quality models), Failures and Errors (definitions, failure modes, failure mechanisms, reliability defects), Faults (fault models, stuck-at faults, bridging faults, timing faults, transistor-level faults, functional-level faults, effectiveness of different fault models based on real data), Logic and Fault Simulation (fault equivalence and fault collapsing, true-value simulation, fault simulation algorithms, statistical methods), Test Generation for Combinational Circuits (algebraic methods, path-tracing (D-alg, PODEM, FAN), testability metrics, test file compression), Digital Design-For-Testability and Internal Scan Design (ad-hoc methods, scan architectures, scan-based test methodology).
2 SWS: (2 SWS + 1,5 x 2 SWS) x 15 + 15 h preparation for the exam = 90 h = 3 ECTS
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Design of Embedded Systems and Computer Architectures
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-105936 | Testing Digital Systems II | Irreg. | 3 | Tahoori |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The objective of this course is to provide more advanced topics on testing of digital systems and complement the foundation covered in Testing Digital Systems I.
Testing of digital circuits plays a critical role during the design and manufacturing cycles. It also ensures the quality of parts shipped to the customers. Test generation and design for testability are integral parts of automated design flow of all electronic products. The objective of this course is to provide more advanced topics on testing of digital systems and complement the foundation covered in Testing Digital Systems I.
Topics include Functional and Structural Testing (design verification vectors, exhaustive test, pseudo-exhaustive test, pseudo-random testing), Essentials of Test Generation for Sequential Circuits (state-machine initialization, time-frame expansion method), Built-in Self Test (test economics of BIST, test pattern generation, output response analysis, BIST architectures), Boundry Scan (Boundry scan architectures, BS test methodology), Delay Testing (path delay test, hazard-free, robust, and non-robust delay tests), transition faults, delay test schemes), Current-Based Testing (motivation, test vectors for IDDQ, variations of IDDQ), Memory Test (memory test algorithm, memory BIST, memory repair), and DFT for System-on-Chip.
2 SWS: (2 SWS + 1.5 x 2 SWS) x 15 + 15 h exam preparation = 90 h = 3 ECTS
Knowledge of Digital Design and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Algorithm Engineering
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114226 | Text-Indexing | WT | 4 | Sanders |
| T-INFO-114227 | Text-Indexing Project/Experiment | WT | 1 | Sanders |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students acquire a systematic understanding of algorithmic issues and solution approaches in the area of text indexing, building on existing knowledge in the subject area of algorithms. They will also be able to
apply learned techniques to related problems and interpret and comprehend current research topics in the area of text indexing.
Upon successful completion of the course, students will be able to:
• explain terms, structures, basic problem definitions, and algorithms from the lecture;
• select which algorithms and data structures are suitable for solving a problem and, if necessary, adapt them to the requirements of a specific problem;
• use algorithms and data structures, analyze them mathematically, and prove the algorithmic properties.
In this lecture we deal with algorithms and data structures for texts, especially text indices. Text indices are data structures that provide additional information about a text in order to accelerate queries regarding this text. These can be simple pattern matching queries ("Does a pattern occur in the text?") or more complex data mining queries ("Which pattern of a certain length occurs most often in the text?").
Furthermore, we deal with text compression. Here, we want to represent a text as space-efficiently as possible. However, we have to make sure that the original text can be reconstructed completely. Here, we speak of lossless compression. In the lecture, we will learn about techniques that are used in compression programs such as gzip.
The lectures including the project/experiment with 5 ECTS corresponds to 150 working hours, which are divided approximately as follows:
• ca. 30 hours attending lectures
• ca. 60 hours preparing and following-up lectures
• ca. 30 hours working on the project/experiment
• ca. 30 hours preparing for the examination
The lecture builds on parts of the contents of the lectures Algorithms I and Algorithms II. Corresponding knowledge is therefore helpful.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
(Usage from 4/1/2026)
Area of Specialization: Algorithm Engineering (Usage from 4/1/2026) Elective Studies in Informatics (Usage from 4/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114982 | Theory of Satisfiability Algorithms | Irreg. | 4 | Künnemann-Goos |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students know the foundations of the rigorous theoretical analysis of algorithms for the Satisfiability problem and its many variants. They have an overview over the state of the art of worst-case time upper bounds. They understand further theoretical aspects such as combinatorial properties, hardness conjectures, etc. and are able to derive implications for the (sub)exponential-time solvability of related problems.
The Satisfiability (SAT) problem for Boolean formulas is fundamental for computer science, due to a variety of applications (cf. the practical success of SAT solvers) as well as its central role for the theory of computation. This course focuses on theoretical aspects of the problem, which includes topics among:
- exponential-time algorithms with provable worst-case guarantees (e.g., for k-SAT, CNF-SAT and further variants)
- tractable special cases of the problem
- classification of constraint satisfaction problems
- combinatorial insights (e.g., the algorithmic Lovasz Local Lemma)
- the sparsification lemma and its limits
- generalizations: MaxSAT, #SAT, Quantified SAT
- hardness hypotheses and their implications: ETH, SETH, NSETH, MASETH, Formula-SETH
- isolation lemma
4 CP correspond to approx. 120 h of work, estimated as follows:
- ca. 30 h attendance in lectures
- ca. 60 h follow-up on lectures, solving exercises, independent review
- ca. 30 h preparation for exam
See Partial Achievements (Teilleistung).
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Theoretical Foundations
Area of Specialization: Software Engineering and Compiler Construction Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115016 | Type Systems and Program Analysis | Irreg. | 6 | Erdweg |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
- Students can understand, design, and implement static type systems.
- Students can validate the soundness of static type systems.
- Students know the foundations of abstract interpretation.
- Students can understand, design, and implement data-flow analyses.
- Students can assess analyses based on their soundness, recall, and precision.
Static type systems and program analyses predict the behavior of programs through source-code analysis, but without executing the program. They are fundamental for isolating ill-defined program behavior (e.g., from type errors), but can also find bugs or also can provide guarantees (e.g., memory safety). Topics covered in this course include the design, definition, formalization, and application of type systems and static program analyses, including type inference, dependent typing, control flow, data flow, abstract interpretation, soundness, and precision.
1. Attendance time 4 h x 15 weeks = 60 h
2. Self-study 7 h x 15 weeks = 105 h
3. Preparation for the exam 15 h
Knowledge of programming semantics is helpful but not required.
Knowledge of logic inference systems is helpful but not required.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Telematics
Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114188 | Ubiquitous Computing | WT | 5 | Beigl |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
The aim of the lecture is to impart knowledge of the fundamentals and advanced methods and techniques of ubiquitous computing. After completing the lecture, students will be able to
reproduce and discuss what they have learnt about existing ubiquitous computing systems.
evaluate the general knowledge of ubiquitous systems and transfer statements and laws to special cases.
evaluate and assess different methods for design processes and user studies and select suitable methods for the development of new solutions.
invent, plan, design and evaluate new ubiquitous systems for use in everyday or industrial process environments and assess the costs and technical implications.
The lecture provides an overview of the history and teaches the concepts, theories and methods of ubiquitous information technology (ubiquitous computing). Based on the appliance concept, students then design their own appliances in the exercise, plan the construction and then develop them. The necessary technical and methodological basics such as hardware for ubiquitous systems, software for ubiquitous systems, principles of context recognition for ubiquitous systems, networking of ubiquitous systems and design of ubiquitous systems and in particular information appliances are discussed. Methods of design and testing for human-machine interaction and human-machine interfaces developed in ubiquitous computing are explained in detail. There is also an introduction to the economic aspects of a ubiquitous system.
In the practical part of the lecture, the understanding of ubiquitous systems is deepened through practical application of the knowledge base of the lecture. The students design and develop their own appliance and test it. The aim is to have gone through the steps towards a prototypical and possibly marketable appliance.
The total workload for this course unit is approximately 150 hours (5.0 credits).
Activity Workload:
- Attendance time: Attendance of the lecture
15 x 90 min; 22 h 30 min
- Attendance time: Attendance of the exercise
15 x 45 min; 11 h 15 min
- Preparation / follow-up of the lecture and exercise
15 x 90 min; 22 h 30 min
- Developing a self-developed concept for an information appliance
33 h 45 min
- Go through set of slides 2x
2 x 12 h; 24 h 00 min
- Prepare exam
36 h 00 min
TOTAL
150 h 00 min
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
(Usage from 10/1/2026)
Area of Specialization: Human-centred Machine Intelligence (Usage from 10/1/2026) Elective Studies in Informatics (Usage from 10/1/2026) |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-115244 | User Studies in Human-Robot Interaction | WT | 6 | Bruno, Neef, Norman |
See Partial Achievements (Teilleistung).
See Partial Achievements (Teilleistung).
Students acquire methodological competencies for independently designing, conducting, and critically evaluating empirical user studies in Human-Robot Interaction (HRI). The focus is on robots interacting with people in social and interactive contexts (e.g., as assistants, companions, tutors, coaches, or collaborative partners).
Students develop the ability to formulate research questions based on existing HRI literature and to select appropriate empirical methods for answering them. This includes quantitative, qualitative, and mixed-methods approaches. Students gain familiarity with central constructs commonly investigated in HRI, such as engagement, trust, acceptance, social presence, and interaction quality.
Particular emphasis is placed on informed methodological decision-making. Students are trained to consider the technical, contextual, and ethical constraints that often arise in HRI studies, especially when robots are evaluated with human participants in realistic or semi-realistic settings. The module draws on perspectives from computer science, engineering, psychology, and the social sciences. Students are able to address both the technical preparation of an interaction scenario and the human-centered evaluation of the resulting interaction.
Through hands-on project work, students gain practical experience in planning and conducting an HRI user study. This includes the development of interaction scenarios, preparation of study materials, participant recruitment, data collection and analysis, and reflection on methodological trade-offs, such as experimental control versus ecological validity. Students also develop the ability to communicate empirical findings and their limitations in written and oral research-oriented formats.
The module focuses on empirical research methods for studying how people interact with robots. It covers the systematic design, execution, analysis, and critical reflection of HRI user studies.
Topics include the formulation of research questions; experimental, quasi-experimental, and exploratory study designs; Wizard-of-Oz methodologies (i.e., robots partly or fully controlled by a human during a study); qualitative and quantitative data collection techniques; mixed-methods approaches; data analysis strategies; and ethical considerations in HRI research. Ethical aspects include informed consent, responsible handling of participant data, and the consideration of participant comfort and safety during human-robot interaction.
Students work in small groups to plan, pilot, and conduct their own HRI user study projects. Depending on the project, this may include interaction design and scripting, study protocols, participant recruitment, data logging, observation, and questionnaire as well as interview-based data collection, and the analysis of behavioral, observational, and self-report data.
Empirical results are interpreted in relation to methodological limitations, contextual factors, and study assumptions. Students critically reflect on the strengths and weaknesses of different study designs and motivate their methodological choices. Group discussions and peer feedback support exchange between student groups and encourage critical engagement with different research approaches.
The module prepares students for independent research projects, Master’s theses, and empirical work in academic or industrial HRI contexts.
The module is designed as a methodological complement to and continuation of the lecture “HRI and Social Robotics”. While the lecture provides a broad introduction to theories, technologies, and interaction capabilities of social robots, "User Studies in Human-Robot Interaction" focuses on the empirical study of human-robot interaction. The two modules are independent but mutually reinforcing.
Attendance is mandatory, as active participation in discussions, group work, and presentations is required.
Course workload:
1) Attendance of interactive course/exercise sessions: 45h (15 x 180 min)
2) Self-study of course material and work on homework assignments: 45h (≈3h/week)
3) Project work (study design, data collection, analysis, reporting): 90 h
Total Load = 180 h
Participation in the lecture “HRI and Social Robotics” or similar introductory courses is recommended but not required. Familiarization with the lecture content is recommended if the course has not been attended.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
Area of Specialization: Robotics and Automation
Area of Specialization: Human-centred Machine Intelligence Elective Studies in Informatics |
| ID | Name | Term offered | Credits | Lecturers |
|---|---|---|---|---|
| Mandatory | ||||
| T-INFO-114145 | Wearable Robotic Technologies | ST | 4 | Asfour, Beigl |
See partial achievements (Teilleistung)
See partial achievements (Teilleistung)
Students can explain the fundamentals, motivation, and applications of wearable robotic technologies including exoskeletons, orthoses, and prostheses. They have comprehensive knowledge of human anatomy, biomechanics, and the neuromuscular system relevant to wearable robot design.
Students understand the core components and challenges associated with the design of wearable robotic technologies, including mechanisms, actuation technologies, sensing approaches, and wearable computing concepts. They can analyze design requirements and challenges including kinematic compatibility and human-robot interfaces for wearable robots.
Students can explain control strategies for prosthetic and exoskeleton systems, understand both physical and cognitive aspects of human-robot interaction, and comprehend concepts such as body schema and symbiotic human-machine interaction in wearable systems.
The lecture provides a comprehensive introduction to wearable robotic technologies. It covers human anatomy and biomechanics as foundations for wearable robot design. Joint biomechanics, locomotion and gait analysis, the neuromuscular system, and models of the human body are discussed to understand how wearable robots can be designed and controlled.
Design principles and mechanisms of wearable robots are addressed. Actuation technologies are covered in depth: requirements analysis based on human motion and user needs, compliant actuation approaches (serial elastic, parallel elastic, variable stiffness actuation), and strategies for increasing efficiency such as energy harvesting, multi-articulation, and underactuation.
The lecture covers wearable computing principles such as body signals and phenomena, design principles for body-worn systems, attachment considerations based on body maps for heat tolerance, weight distribution, and flexibility, and fabric design with textile-embedded electronics.
Exoskeleton systems are presented, covering application areas, design challenges such as redundancy, kinematic compatibility, and misalignments, various types of exoskeletons, and the general design process. Prosthetic systems cover types of hand and lower limb prostheses, hand synergies for control, and mechanical interfaces through socket design.
Human-robot interaction addresses both physical and cognitive interfaces. Control strategies are presented for prosthetic systems (semi-autonomous grasping) and exoskeleton systems (hierarchical control, assistance evaluation). The concept of body schema from both neuroscience and robotics perspectives is discussed, emphasizing symbiotic human-machine interaction in tightly coupled wearable systems. State-of-the-art examples across various application domains illustrate current research and developments.
Lecture with 2 SWS, 4 ECTS.
4 ECTS corresponds to ca. 120 hours, thereof
ca. 15 * 2h = 30h attendance in the lecture
ca. 15 * 3h = 45h preparation and follow-up of the lecture
ca. 45h preparation for the exam and exam itself
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107836 - Practical Course: Decentralized Learning Lab |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400102 | Practical Course: Decentralized Learning Lab | 1 SWS | Practical course / 🗣 | Hartenstein, Hemken |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The following partial aspects are included in the grading: Presentation (duration approx. 20 min.) and a corresponding report. The grading scale will be announced in the course. Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
None.
Basic knowledge of Machine Learning and Decentralized Systems is helpful.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106303 - Access Control Systems: Models and Technology |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400147 | Access Control Systems: Models and Technology | 3 SWS | Lecture / Practice / 🗣 | Hartenstein, Hemken |
| Exams | |||||
| ST 2026 | 7500176 | Access Control Systems: Models and Technology | Hartenstein | ||
| WT 26/27 | 7500087 | Access Control Systems: Models and Technology | Hartenstein | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 Nr. 1 SPO) lasting 60 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 Abs. 3 SPO) whether the examination takes place
• in the form of an oral examination lasting 30 minutes pursuant to § 4 Abs. 2 Nr. 2 SPO or
• in the form of a written examination lasting 60 minutes in accordance with § 4 Abs. 2 Nr. 1 SPO.
None.
Basics according to the lectures "Information Security" and "IT Security Management for Networked Systems" are recommended.
| Coordinators: |
Prof. Dr. Nadja Klein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106812 - Advanced Bayesian Data Analysis |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400120 | Advanced Bayesian Data Analysis | 3 SWS | Lecture / Practice / 🗣 | Klein |
| Exams | |||||
| ST 2026 | 7500233 | Nachklausur Advanced Bayesian Data Analysis | Klein | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
A bonus can be acquired through successful participation in the exercise as a success control of a different kind (§4(2), 3 SPO 2008) or study performance (§4(3) SPO 2015). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4). The bonus is only valid for the main and post exams of the semester in which it was earned. After that, the grade bonus expires.
- Knowledge in R or Python
- Mathematics-heavy lecture. The basics will be reviewed, but mathematical proficiency is helpful
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107863 - Advanced Competitive Programming |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400145 | Advanced Competitive Programming | 2 SWS | Lecture / 🗣 | Künnemann-Goos, Bläsius |
| Exams | |||||
| WT 26/27 | 7500317 | Advanced Competitive Programming | Künnemann-Goos, Bläsius | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The grade will be determined by the performance in solving programming assignments in the form of homework contests, live contests and a final exam contest. The grading scale will be announced in the course. Students may redraw from the examination until the second live contest.
None.
The course assumes basic knowledge of algorithms and data structures (e.g., lectures Algorithms 1 and 2) and coding in C++.
We strongly recommend successful participation in “Basispraktikum zum ICPC Programmierwettbewerb”, a similar course or basic experience with competitive programming contests.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107200 - Advanced Data Structures |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400164 | Advanced Data Structures | 3 SWS | Lecture / 🗣 | Sanders, Hermann, Walzer, Groot Koerkamp |
| Exams | |||||
| ST 2026 | 7500155 | Advanced Data Structures | Sanders | ||
| WT 26/27 | 7500538 | Advanced Data Structures | Sanders | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
The lecture builds on parts of the contents of the lectures Algorithms I and Algorithms II. Corresponding knowledge is therefore helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107200 - Advanced Data Structures |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400164 | Advanced Data Structures | 3 SWS | Lecture / 🗣 | Sanders, Hermann, Walzer, Groot Koerkamp |
| Exams | |||||
| ST 2026 | 7500135 | Advanced Data Structures Project/Experiment | Sanders | ||
| WT 26/27 | 7500216 | Advanced Data Structures Project/Experiment | Sanders | ||
The examination takes place in the form of an an examination of another type (§ 4 Abs. 2 No. 3 SPO) in form of a project/experiment.
An overall grade is awarded.
None.
The lecture builds on parts of the contents of the lectures Algorithms I and Algorithms II. Corresponding knowledge is therefore helpful.
| Coordinators: |
Prof. Dr. Maxim Ulrich
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-105659 - Advanced Machine Learning and Data Science |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2500016 | Advanced Machine Learning and Data Science | 4 SWS | Project / 🧩 | Ulrich |
| WT 26/27 | 2500063 | Advanced Machine Learning and Data Science | 4 SWS | Project / 🧩 | Ulrich |
| Exams | |||||
| ST 2026 | 7900378 | Advanced Machine Learning and Data Science | Ulrich | ||
| WT 26/27 | 7900378 | Advanced Machine Learning and Data Science | Ulrich | ||
The assessment is carried out as a single examination of another type. It includes the interim presentations during the project, the quality of the implementation, the written final paper, and the final presentation. All components are integrated into a single overall assessment, and the resulting grade reflects the overall impression of the combined performance.
The course is aimed at students majoring in data science and/or machine learning. It offers students the opportunity to gain practical knowledge of new developments in the fields of data science and machine learning.
Kick-off Meeting for Winter Term 2025/26:
We will introduce project topics designed for students of computer science, engineering, and mathematics. The meeting will provide an overview of the course structure, project topics, and expectations for the semester.
After the meeting (within 48 hours): Please submit your Priority A and Priority B topic preferences via the form within 48 hours after the kick-off.
If you need more information, please visit https://risk.fbv.kit.edu/2307.php.
| Coordinators: |
Prof. Dr. Martin Klarmann
Anja Konhäuser
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7900297 | Digital Marketing and Sales in B2B | Klarmann | ||
Alternative exam assessment according to § 4 paragraph 2 Nr. 3 of the examination regulation. (team presentation of a case study with subsequent discussion totalling 30 minutes).
None.
The course “AI- and Data-Driven B2B Sales” provides a comprehensive understanding of how digital and AI-enabled business models are designed, scaled, and evaluated. Particular emphasis is placed on the role of data and artificial intelligence throughout the customer journey and their impact on both B2B and B2C environments. Students learn how to analyze structured and unstructured data, apply relevant KPIs, and transform data- and AI-driven insights into tangible business value. The course content is reinforced through a practical case study in which students develop a business case based on data, assumptions, and forecasts, and evaluate its economic impact.
Participation requires an application. The application period starts at the beginning of the semester. More information can be obtained on the website of the research group Marketing and Sales (marketing.iism.kit.edu). Access to this course is restricted. Typically all students will be granted the attendance of one course with 1.5 ECTS. Nevertheless attendance can not be guaranteed.For further information please contact Marketing and Sales Research Group (marketing.iism.kit.edu).Please note that only one of the 1.5-ECTS courses can be attended in this module.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100795 - Algorithm Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400022 | Algorithm Engineering | 3 SWS | Lecture / 🗣 | Sanders, Hermann |
| WT 26/27 | 2400021 | Algorithm Engineering | Lecture / 🗣 | Sanders, Seemaier | |
| Exams | |||||
| ST 2026 | 7500276 | Algorithm Engineering | Sanders | ||
| ST 2026 | 75514 | Algorithm Engineering | Sanders | ||
| WT 26/27 | 75514 | Algorithm Engineering | Sanders | ||
The assessment is carried out as an oral examination lasting approximately 20 minutes (§ 4 Abs. 2 Nr. 2 SPO).
none.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100795 - Algorithm Engineering |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500339 | Algorithm Engineering Pass | Sanders | ||
| WT 26/27 | 7500187 | Algorithm Engineering Pass | Sanders | ||
The assessment is carried out as an examination of another type (§ 2 Abs. 2 Nr. 3).
The exercise can be evidenced by various performance records. This is determined individually during the lecture. Usually, the student prepares a seminar presentation and/or works on a practical tasks with written elaboration and evaluation (the main performance consists of the programming, documented by the source code that is to be handed in and supplemented by a short written report).
Students may redraw from the examination during the first four weeks after they have been assigned a task.
An overall grade is awarded.
None.
| Coordinators: |
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106960 - Algorithmic Graph Theory |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400028 | Algorithmic Graph Theory | 3 SWS | Lecture / Practice / 🗣 | Ueckerdt |
| Exams | |||||
| ST 2026 | 7500238 | Algorithmic Graph Theory | Ueckerdt | ||
| WT 26/27 | 7500281 | Algorithmic Graph Theory | Ueckerdt | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of the basics of graph theory and algorithm technology is helpful
| Coordinators: |
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106961 - Algorithms for Visualization of Graphs |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400155 | Graph Drawing | 3 SWS | Lecture / Practice / 🗣 | Ueckerdt |
| Exams | |||||
| ST 2026 | 7500404 | Graph Drawing | Ueckerdt | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of the basics of graph theory and algorithm technology is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107201 - Algorithms II |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424079 | Algorithms II | 4 SWS | Lecture / 🗣 | Sanders, Künnemann-Goos, Stieß, Gokaj |
| Exams | |||||
| ST 2026 | 7500080 | Algorithms II - Nachklausur | Künnemann-Goos | ||
| WT 26/27 | 7500245 | Algorithms II | Künnemann-Goos | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
None.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106810 - Artificial Intelligence & IT-Security |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 24000 | Artificial Intelligence & IT-Security | 4 SWS | Lecture / Practice / 🗣 | Wressnegger |
| Exams | |||||
| WT 26/27 | 7500396 | Artificial Intelligence & IT-Security | |||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
Depending on the number of participants, it will be announced six weeks before the examination performance (§ 6 Abs. 3 SPO) whether the performance review will be
takes place.
None.
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-104447 - Automated Planning and Scheduling |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400108 | Nicht im SoSe 2026! Automated Planning and Scheduling | Lecture / Practice | Schreiber, Sanders | |
| WT 26/27 | 2400026 | Nicht im WS 2026/27! - Automated Planning and Scheduling | Lecture / Practice | Schreiber, Sanders | |
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 30 minutes.
None.
| Coordinators: |
Kevin Simon
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-107806 - Autonomous Driving in Practice |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2113820 | Autonomous Driving in Practice | 3 SWS | Lecture / Practice / 🗣 | Frey, Simon |
| Exams | |||||
| WT 26/27 | 76-T-MACH-115202 | Autonomous Driving in Practice | Simon | ||
The final grade is based on: 2 written exams (duration about 30 minutes) and the final presentation of the practical task (duration 30 minutes max.), including the final report (2-3 pages). Submission of the homework is mandatory.
The overall impression will be evaluated.
Prior experience in autonomous driving, automotive engineering, and machine learning is a plus.
Due to capacity limitations, the lecture is limited to 16 students. If necessary, a selection process will be conducted. Places will be allocated based on the students’ academic progress (semester level and subject-specific programming skills).
The course will be conducted in English.
| Coordinators: |
Prof. Dr. Rudolph Triebel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106608 - Autonomous Learning for Intelligent Robot Perception |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400213 | Autonomous Learning for Intelligent Robot Perception | 2 SWS | Lecture / Practice / 🗣 | Triebel |
| Exams | |||||
| ST 2026 | 7500423 | Autonomous Learning for Intelligent Robot Perception | Triebel | ||
| WT 26/27 | 7500373 | Autonomous Learning for Intelligent Robot Perception | Triebel | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
None.
A basic understanding of probability theory and linear algebra is required
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107599 - Beating the Worst-Case in Practice: Unexpectedly Efficient Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400121 | Beating the Worst Case in Practice: Surprisingly Efficient Algorithms | 4 SWS | Practical course / 🗣 | Wilhelm, von der Heydt, Bläsius |
| WT 26/27 | 2400253 | Beating the Worst-Case in Practice: Unexpectedly Efficient Algorithms | 4 SWS | Practical course / 🗣 | Bläsius, von der Heydt, Göttlicher |
| Exams | |||||
| WT 26/27 | 7500368 | Beating the Worst-Case in Practice: Unexpectedly Efficient Algorithms | Bläsius | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). An implementation and a written elaboration must be prepared.
None.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-106903 - Biologically Inspired Robots |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2122330 | Biologically Inspired Robots | 2 SWS | Lecture / 🗣 | Rönnau |
| Exams | |||||
| ST 2026 | 76-T-MACH-113856 | Biologically Inspired Robots | Rönnau | ||
| ST 2026 | 76-T-MACH-113856-mdl | Biologically Inspired Robots | Rönnau | ||
| WT 26/27 | 76-T-MACH-113856 | Biologically Inspired Robots | Rönnau | ||
Success is assessed in the form of an oral examination (approx. 15-20 minutes)
none
It is recommended to listen to the course "Robotics I" beforehand .
The course is offered in English.
| Coordinators: |
Prof. Dr. Werner Nahm
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-101834 - Business Innovation in Optics and Photonics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2305742 | Business Innovation in Optics and Photonics | 2 SWS | Lecture / 🗣 | Riedel, Nahm |
| WT 26/27 | 2305743 | Erxercise for 2305742 Business Innovation in Optics and Photonics | 1 SWS | Practice / 🗣 | Riedel, Nahm |
| Exams | |||||
| WT 26/27 | 7305742 | Business Innovation in Optics and Photonics | Nahm, Riedel | ||
Type of Examination: examination of another type
Duration of Examination: 4 group presentations à 20 minutes (approx.)
Modality of Exam: The exam consists of four group presentations. 2nd day: Technology Presentation. 3rd day: Development plan presentation. 4th day: Business Canvas presentation. Final presentation: Business pitch. The overall impression is rated.
none
Good knowledge in optics & photonics. Personal motivation and interest for getting deeper into business development aspects, methods and tools. Commitment to active, regular and coninuous participation in the group work.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-106905 - CAD Engineering Project for Intelligent Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2122331 | CAD Engineering Project for Intelligent Systems | 4 SWS | Project / 🗣 | Rönnau |
| WT 26/27 | 2122331 | CAD Engineering Project for Intelligent Systems | 4 SWS | Seminar / 🗣 | Rönnau |
| Exams | |||||
| ST 2026 | 76-T-MACH-113857 | CAD Engineering Project for Intelligent Systems | Rönnau | ||
Success control takes place in the form of another type of examination. Design project as well as written elaboration in a team and a final presentation. The overall impression is rated. Further details will be provided at the beginning of the course.
none
The course is offered in English.
| Coordinators: |
Prof. Dr.-Ing. Laurent Schmalen
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-105616 - Channel Coding: Algebraic Methods for Communications and Storage |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2310546 | Channel Coding: Algebraic Methods for Communications and Storage | 2 SWS | Lecture / 🗣 | Fengler, Schmalen |
| Exams | |||||
| ST 2026 | 7310546-1 | Channel Coding: Algebraic Methods for Communications and Storage | Fengler | ||
| WT 26/27 | 7310546-1 | Channel Coding: Algebraic Methods for Communications and Storage | Fengler | ||
The exam is held as an oral exam of approx. 20 min.
The module grade of the module corresponds to the grade of the oral exam.
none
Previous attendance of the lectures "Communication Engineering I" and "Probability Theory" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Laurent Schmalen
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-105617 - Channel Coding: Graph-Based Codes |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2310520 | Channel Coding: Graph-Based Codes | 3 SWS | Lecture / 🗣 | Schmalen |
| WT 26/27 | 2310521 | Exercise for 2310520 Channel Coding: Graph-Based Codes | 1 SWS | Practice / 🗣 | Schmalen |
| Exams | |||||
| ST 2026 | 7310520-1 | Channel Coding: Graph-Based Codes | Schmalen | ||
| WT 26/27 | 7310520-1 | Channel Coding: Graph-Based Codes | Schmalen | ||
The success control takes place in the form of an oral examination lasting approx. 25 minutes. Before the examination, there is a preparation phase of 30 minutes in which preparatory tasks are solved.
none
Previous attendance of the lectures "Communication Engineering I" and "Theory of Probability" is recommended. Knowledge from the lectures "Applied Information Theory" and "Verfahren der Kanalcodierung" is helpful.
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
M-MATH-102950 - Combinatorics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 0150300 | Combinatorics | 4 SWS | Lecture | Aksenovich, Sagdeev |
| ST 2026 | 0150310 | Tutorial for Combinatorics (0150300) | 2 SWS | Practice | Aksenovich, Sagdeev |
| Exams | |||||
| ST 2026 | 00009 | Combinatorics | Aksenovich | ||
| WT 26/27 | 7700076 | Combinatorics | Aksenovich | ||
none
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106966 - Compiler Design |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400055 | Compiler Design | 4 SWS | Lecture / 🗣 | Platzer |
| Exams | |||||
| ST 2026 | 7500151 | Compiler Design | Platzer | ||
The assessment is usually carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
In order to receive a bonus, you must earn at least 50% of the points for solving the exercises. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4).
None.
Students are expected to have significant experience in a high-level programming language. Students are also expected to follow the lecture notes.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107228 - Computational Geometry |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400119 | Computational Geometry | 4 SWS | Lecture / Practice / 🗣 | Bläsius, Yi, Wilhelm, von der Heydt |
| WT 26/27 | 2400156 | Computational Geometry | 4 SWS | Lecture / Practice / 🗣 | Bläsius, Yi, von der Heydt, Schmidt |
| Exams | |||||
| ST 2026 | 7500247 | Computational Geometry | Bläsius | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
PLUS: The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). A total of two repetitions are possible.
None.
Basic knowledge of algorithms and data structures (e.g., from the courses Algorithms 1 + 2) is expected.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107228 - Computational Geometry |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400119 | Computational Geometry | 4 SWS | Lecture / Practice / 🗣 | Bläsius, Yi, Wilhelm, von der Heydt |
| WT 26/27 | 2400156 | Computational Geometry | 4 SWS | Lecture / Practice / 🗣 | Bläsius, Yi, von der Heydt, Schmidt |
| Exams | |||||
| ST 2026 | 7500034 | Computational Geometry - Pass | Bläsius | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). A total of two repetitions are possible.
None.
Basic knowledge of algorithms and data structures (e.g., from the courses Algorithms 1 + 2) is expected.
| Coordinators: |
Dr.-Ing. Johannes Meyer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106190 - Computational Imaging |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400173 | Computational Imaging | 3 SWS | Lecture / Practice / 🗣 | Meyer, Beyerer |
| Exams | |||||
| ST 2026 | 7500351 | Computational Imaging | Beyerer | ||
| WT 26/27 | 7500276 | Computational Imaging | Beyerer | ||
The assessment takes the form of a written examination, usually lasting 60 minutes in accordance with Section 4 (2) No. 1 SPO.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take place
- in the form of an oral examination in accordance with Section 4 (2) No. 2 SPO or
- in the form of a written examination in accordance with Section 4 (2) No. 1 SPO.
None.
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106256 - Constructive Logic |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400097 | Constructive Logic (findet im SoSe 26 nicht statt!) | 4 SWS | Lecture / 🗣 | Platzer |
| Exams | |||||
| ST 2026 | 7500206 | Constructive Logic | Platzer | ||
The assessment is usually carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
You will be expected to follow the lecture notes.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107435 - Cryptographic Protocols II: Universal Composability |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400022 | Cryptographic Protocols II: Universal Composability | 2 SWS | Lecture / 🗣 | Müller-Quade, Mechler |
| Exams | |||||
| ST 2026 | 7500179 | Cryptographic Protocols II: Universal Composability | Müller-Quade, Mechler | ||
| WT 26/27 | 7500296 | Cryptographic Protocols II: Universal Composability | Müller-Quade, Mechler | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) approx. 30 minutes.
None.
Students should be familiar with the contents of the modules "Theoretische Grundlagen der Kryptographie" (Theoretical Foundations of Cryptography) and „Kryptographische Protokolle“ (Cryptographic Protocols I).
| Coordinators: |
TT-Prof. Dr. Benjamin Schäfer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106655 - Data Science and Artificial Intelligence for Energy Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400098 | Data science and Artificial Intelligence for Energy Systems | 2 SWS | Lecture / 🗣 | Schäfer |
| ST 2026 | 2400173 | Data science and Artificial Intelligence for Energy Systems (Excercise) | 2 SWS | Practice / 🗣 | Schäfer |
| WT 26/27 | 2400079 | Data Science and Artificial Intelligence for Energy Systems | 2 SWS | Lecture / 🗣 | Schäfer |
| WT 26/27 | 2400082 | Data Science and Artificial Intelligence for Energy Systems | 2 SWS | Practice / 🗣 | Schäfer, Nikoltchovska, Sadric |
| Exams | |||||
| ST 2026 | 7500309 | Data Science and Artificial Intelligence for Energy Systems | Schäfer | ||
| WT 26/27 | 7500398 | Data Science and Artificial Intelligence for Energy Systems | Schäfer | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting about 30 minutes.
None.
Knowledge of AI basics is very helpful.
Previous participation in “Energieinformatik 1” and/or “Energieinformatik 2” is beneficiary but not mandatory.
Knowledge of Python is highly recommended.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105334 - Decentralized Systems: Fundamentals, Modeling, and Applications |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400089 | Decentralized Systems: Fundamentals, Modeling, and Applications | 4 SWS | Lecture / Practice / 🗣 | Hartenstein, Jacob |
| Exams | |||||
| ST 2026 | 7500070 | Decentralized Systems: Fundamentals, Modeling, and Applications | Hartenstein | ||
| ST 2026 | 7500284 | Decentralized Systems: Fundamentals, Modeling, and Applications | Hartenstein | ||
| WT 26/27 | 7500141 | Decentralized Systems: Fundamentals, Modeling, and Applications | Hartenstein | ||
| WT 26/27 | 7500371 | Decentralized Systems: Fundamentals, Modeling, and Applications | Hartenstein | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 Abs. 3 SPO) whether the examination takes place
in the form of an oral examination lasting 30 minutes pursuant to § 4 Abs. 2 Nr. 2 SPO or
in the form of a written examination lasting 60 minutes in accordance with § 4 Abs. 2 Nr. 1 SPO.
None.
Basics according to the lectures "Information Security" and "Introduction to Computer Networks" are recommended.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107197 - Deep Learning and Neural Networks |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400024 | Deep Learning and Neural Networks | 4 SWS | Lecture / 🗣 | Niehues |
| Exams | |||||
| ST 2026 | 7500044 | Deep Learning and Neural Networks | Niehues | ||
| WT 26/27 | 7500400 | Deep Learning and Neural Networks (Nachklausur) | Niehues | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
T-INFO-101383 - Neural networks must not be started.
Prior successful completion of the core module "Cognitive Systems" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Rainer Stiefelhagen
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107843 - Deep Learning for Computer Vision: Advanced Topics |
Success is assessed in the form of a written examination (lasting 60 minutes) in accordance with § 4 Abs. 2 No. 1 SPO .
None.
Knowledge of deep learning basics is strongly recommended.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107230 - Design and Architectures of Embedded Systems (ESII) |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424106 | Design and architectures of embedded systems (ES2) | 2 SWS | Lecture | Khdr, Henkel |
| Exams | |||||
| ST 2026 | 7500037 | VL: Design and architectures of embedded systems (ES2) | Henkel | ||
| WT 26/27 | 7500124 | VL: Design and architectures of embedded systems (ES2) | Henkel | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of computer structures is helpful.
| Coordinators: |
Prof. Dr. Ann-Kristin Kupfer
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2571185 | Digital Marketing | 2 SWS | Lecture / 🗣 | Kupfer |
| ST 2026 | 2571186 | Digital Marketing Exercise | 1 SWS | Practice / 🗣 | Schmitt, Cinar, Kupfer |
| Exams | |||||
| ST 2026 | 7900064 | Digital Marketing | Kupfer | ||
| WT 26/27 | 7900021 | Digital Marketing | Kupfer | ||
Success is assessed in the form of an examination of another type. The following aspects are included in the assessment:
Further details on the organization of the performance and the points system for the assessment will be announced in the lecture.
None
Students are highly encouraged to actively participate in class.
| Coordinators: |
Prof. Dr. Achim Streit
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107215 - Distributed Computing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400050 | Distributed Computing | 2 SWS | Lecture / 🗣 | Streit, Schnepf, Gudu |
| Exams | |||||
| WT 26/27 | 7500322 | Distributed Computing | Streit | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes. Depending on the number of participants it will be announced six weeks before the assessment (§3 Abs. 3 SPO) if the assessment is done
• as an oral examination according to § 4 Abs. 2 No. 2 SPO or
• as a written examination according to § 4 Abs. 2 No. 1 SPO.
None.
Knowledge in the area of computer networks helpful.
| Coordinators: |
Prof. Dr. Benjamin Scheibehenne
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2500059 | Economic Decision Making | 3 SWS | Lecture / Practice / 🗣 | Scheibehenne |
| Exams | |||||
| WT 26/27 | 7900173 | Economic Decision Making | Scheibehenne | ||
Alternative exam assessment. The grading includes the following aspects:
A written exam (60 minutes)
A presentation during the exercise
The scoring system for the grading will be announced at the beginning of the course.
Due to the interactive nature of the class, the number of participants is limited to 30 students. Registration via the WiWi Portal is required for participation in the class. Further information on participant selection will be provided in the WiWi Portal.
The judgments and decisions that we make can have long ranging and important consequences for our (financial) well-being and individual health. Hence, the goal of this lecture is to gain a better understanding of how people make judgments and decisions and the factors that influences their behavior. We will look into simple heuristics and mental shortcuts that decision makers use to navigate their environment, in particular so in an economic context. Following this, the lecture will provide an overview into social and emotional influences on decision making. In the second half of the semester we will look into some more specific topics including self-control, nudging, and food choice. The last part of the lecture will focus on risk communication and risk perception. We will address these questions from an interdisciplinary perspective at the intersection of Psychology, Behavioral Economics, Marketing, Cognitive Science, and Biology. Across all topics covered in class, we will engage with basic theoretical work as well as with groundbreaking empirical research and current scientific debates.
The workload of the class is 4.5 ECTS. This consists of 3 ETCS for the lecture and 1.5 ETCS for the Übung. Details about the Übung will be communicated at the first day of the class. The class will take place twice a week during the first half of the wintersemester. The exam will take place before Christmas.
| Coordinators: |
Dr. Victor Pankratius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107234 - Edge-AI in Software and Sensor Applications |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400006 | EdgeAI in Software and Sensor Applications | 2 SWS | Lecture / 🖥 | Pankratius |
| WT 26/27 | 2400124 | EdgeAI in Software and Sensor Applications | 2 SWS | Lecture / 🖥 | Pankratius |
| Exams | |||||
| ST 2026 | 7500196 | Edge-AI in Software and Sensor Applications | Pankratius | ||
| WT 26/27 | 7500303 | Edge-AI in Software and Sensor Applications | Pankratius | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
Basic studies in computer science
Knowledge of e.g. cognitive systems, software engineering, algorithms, computer networks & structures, low-power design is helpful.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105775 - Embedded Machine Learning Lab |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400295 | Embedded Machine Learning Lab | 4 SWS | Practical course / 🗣 | Henkel, Ahmed, Dietrich, Khdr |
| WT 26/27 | 2400295 | Embedded Machine Learning Lab | 4 SWS | Practical course / 🗣 | Henkel, Ahmed, Dietrich |
| Exams | |||||
| ST 2026 | 7500321 | Embedded Machine Learning Lab | Henkel | ||
| WT 26/27 | 7500295 | Embedded Machine Learning Lab | Henkel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of a practical assignment, presentations and, if applicable, a written paper. The written paper, presentations and practical work are weighted according to the course.
None.
This lab requires a basic (theoretic) knowledge about neural networks and training. Further knowledge of Linux environments and Python is strongly advised since they will be intensively used in the lab and are the de-facto industry standard for machine learning research.
| Coordinators: |
Raziyeh Dehghani
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107685 - Empirical Software Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424156 | Empirical Software Engineering | 2 SWS | Lecture / 🗣 | Koziolek, Dehghani, Kaplan |
| Exams | |||||
| ST 2026 | 7500053 | Empirical Software Engineering | Gerking | ||
| ST 2026 | 7500372 | Empirical Software Engineering | Koziolek | ||
| WT 26/27 | 7500312 | Empirical Software Engineering | Koziolek | ||
The assessment is carried out as an oral examination, lasting approx. 30 minutes in accordance with Section 4 (2) No. 2 SPO.
None.
Familiarity with statistical analysis methods is highly recommended.
| Coordinators: |
Prof. Dr. Raffaela Mirandola
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106626 - Engineering Self-Adaptive Systems |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400186 | Engineering Self-Adaptive Systems | 2 SWS | Lecture | Mirandola |
| Exams | |||||
| WT 26/27 | 7500381 | Engineering Self-Adaptive Systems | Mirandola | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
| Coordinators: |
Prof. Dr. Orestis Terzidis
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-ETIT-105073 - Student Innovation Lab |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2545001 | Entrepreneurship | 2 SWS | Lecture / 🗣 | Terzidis, Dang |
| WT 26/27 | 2545001 | Entrepreneurship | 2 SWS | Lecture / 🧩 | Terzidis, Dang |
| Exams | |||||
| ST 2026 | 7900002 | Entrepreneurship | Terzidis | ||
| ST 2026 | 7900192 | Entrepreneurship | Terzidis | ||
| ST 2026 | 7900376 | Entrepreneurship | Terzidis | ||
| ST 2026 | 7900377 | Entrepreneurship | Terzidis | ||
| WT 26/27 | 7900045 | Entrepreneurship | Terzidis | ||
| WT 26/27 | 7900229 | Entrepreneurship | Terzidis | ||
The assessment consists of a written exam (60 minutes) (following §4(2), 1 of the examination regulation).
Students are offered the opportunity to earn a grade bonus through separate assignments. If the grade of the written exam is between 4.0 and 1.3, the bonus improves the grade by a maximum of one grade level (0.3 or 0.4). The exact criteria for awarding a bonus will be announced at the beginning of the lecture.
None
None
| Coordinators: |
Gustavo Gil Gasiola
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107030 - EU Data Protection Law |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424019 | EU Data Protection Law | 2 SWS | Lecture / 🗣 | Gil Gasiola |
| Exams | |||||
| WT 26/27 | 7500378 | EU Data Protection Law | Zufall | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
None
Competency Goals:
Students are able to comprehend the EU data protection regulation, including the General Data Protection Regulation and related EU data regulations.
They know the foundations of data protection rules, including fundamental concepts (e.g., “personal data”, “processing”, “data subject”). They are also familiar with the principles of personal data processing (lawfulness, limited purpose, transparency, accountability) as well as the rights of the data subject.
They can identify the main obligations of the controller and the processor.
Students understand the conditions for the transfer of personal data to third countries.
They can identify the other regulations that govern data in the European Union.
Students are able to read and understand legal text related to data regulation.
They can understand and solve simple data protection cases.
Content:
The General Data Protection Regulation (GDPR) of the European Union is a milestone in protecting individuals from the unlawful use of their data. In a data-driven society, economy, and government, this protection has become essential to guarantee fundamental rights. In addition to its direct impact on the legal systems of all Member States, the GDPR has a major influence on third countries that have adopted similar regulations (e.g. Switzerland, Argentina, Brazil, South Africa, and many others). In this way, the EU Data Protection Regulation has established itself as the “gold standard” of data protection, providing guidance to address the challenges posed by new technologies and new ways of creating, using and sharing personal data. Understanding the structure of data protection in the EU is therefore essential to grasp its impact on individual rights, public administration, business models, and even technological development.
This lecture aims to provide a structured overview of the EU Data Protection Regulation, and to offer tools to understand the regulatory structure of the EU Data Regulation. The lecture will cover the following topics:
- Introduction to EU law
- Development of the EU data protection regulation
- Legal structure of data protection in the EU
- Role of national and sectoral laws
- Data protection as fundamental right
- Principles of data protection
- Lawfulness of personal data processing
- Anonymization and pseudonymization of personal data
- Special categories of personal data
- Rights of the data subject
- Transfer of personal data to third countries
- Responsibility of the controller and the processor
- Security of personal data and personal data breach
- Open Data Directive
- Data Governance Act
- Data Act
Workload
- Attendance time to the lectures = 15 x 90 min = 22 h 30 min
- Self-study during the semester = 47 h 30 min
- Preparation for the exam = 20 h
- Total = 90 h
| Coordinators: |
TT-Prof. Dr. Frederike Zufall
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-108019 - EU-Law |
Depending on the number of attending students, the assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting approximately 20 minutes or as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes. The type of assessment will be announced at least six weeks before the end of the course.
Students are advised to have the EU Treaties and other relevant legal texts at hand (e.g. Blackstone’s EU Treaties & Legislation or online at https://eur-lex.europa.eu/collection/eu-law/treaties/treaties-force.html ).
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106302 - Explainable Artificial Intelligence |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400128 | Explainable Artificial Intelligence | 2 SWS | Lecture / 🗣 | Lioutikov |
| Exams | |||||
| ST 2026 | 7500359 | Explainable Artificial Intelligence | Lioutikov | ||
| WT 26/27 | 7500370 | Explainable Artificial Intelligence | Lioutikov | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
A bonus can be acquired through successful participation in the exercise as a success control of a different kind (§4(2), 3 SPO 2008) or study performance (§4(3) SPO 2015). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4). The bonus is only valid for the main and post exams of the semester in which it was earned. After that, the grade bonus expires.
None.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python / PyTorch experience could be beneficial when we discuss practical examples/implementations.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106644 - Fine-Grained Complexity Theory & Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400152 | Fine-Grained Complexity Theory & Algorithms | 4 SWS | Lecture / Practice / 🗣 | Künnemann-Goos |
| WT 26/27 | 2400104 | Fine-grained Algorithm Design and Engineering | 4 SWS | Practical course / 🗣 | Künnemann-Goos, Angrick, Gokaj |
| Exams | |||||
| ST 2026 | 7500346 | Fine-Grained Complexity Theory & Algorithms | Künnemann-Goos | ||
| WT 26/27 | 7500042 | Fine-Grained Algorithm Design and Engineering | Künnemann-Goos | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20-30 minutes.
None.
Basic knowledge of theoretical computer science and algorithm design is recommended.
| Coordinators: |
Jun.-Prof. Dr. Jan Stühmer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106237 - Geometric Deep Learning |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400179 | Geometric Deep Learning | 4 SWS | Lecture / Practice / 🗣 | Stühmer, Berndt |
| Exams | |||||
| ST 2026 | 7500374 | Geometric Deep Learning | Stühmer | ||
| WT 26/27 | 7500338 | Geometric Deep Learning | Stühmer | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge about the foundations of machine learning, group theory and linear algebra useful but not required.
| Coordinators: |
Prof. Dr. Peter Sanders
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107211 - Graph Partitioning and Graph Clustering in Theory and Practice |
| Exams | |||||
|---|---|---|---|---|---|
| WT 26/27 | 75722 | Graph partitioning and graph clustering in theory and practice | Sanders | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
The module grade is made up of the graded and weighted performance assessments (usually 80% of the oral examination and 20% of the other performance).
None.
Knowledge of graph theory and algorithm technology is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107211 - Graph Partitioning and Graph Clustering in Theory and Practice |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). (seminar paper/presentation/programming task or similar).
The module grade is made up of the graded and weighted performance assessments (usually 80% of the oral examination and 20% of the other performance). An overall grade is awarded.
None.
Knowledge of graph theory and algorithm technology is helpful.
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
M-MATH-101336 - Graph Theory |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 0104500 | Graph Theory | 4 SWS | Lecture | Aksenovich, Liu |
| WT 26/27 | 0104510 | Tutorial for 0104500 (Graph Theory) | 2 SWS | Practice | Aksenovich, Liu |
| Exams | |||||
| ST 2026 | 7700108 | Graph Theory | Aksenovich | ||
None
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-101573 - Hands-on Bioinformatics Practical |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Practical tasks in the field of bioinformatics must be completed. The results must be presented in writing or orally.
The exam in Introduction to Bioinformatics for Computer Scientists must have been passed in one of the preceding semesters.
| Coordinators: |
Dr.-Ing. Jens Becker
Prof. Dr.-Ing. Jürgen Becker
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-100449 - Hardware Modeling and Simulation |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2311608 | Hardware Modeling and Simulation | 2 SWS | Lecture / 🗣 | Becker, Becker |
| WT 26/27 | 2311610 | Tutorial for 2311608 Hardware Modeling and Simulation | 1 SWS | Practice / 🗣 | Sotiropoulos |
| Exams | |||||
| ST 2026 | 7311608 | Hardware Modeling and Simulation | Becker, Becker | ||
| WT 26/27 | 7311608 | Hardware Modeling and Simulation | Becker, Becker | ||
Achievement is examined in the form of a written examination lasting 120 minutes.
none
| Coordinators: |
Prof. Dr.-Ing. Jürgen Becker
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-106963 - Hardware Synthesis and Optimization |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2311619 | Hardware Synthesis and Optimization | 3 SWS | Lecture / 🗣 | Becker |
| ST 2026 | 2311621 | Tutorial for 2311619 Hardware Synthesis and Optimization | 1 SWS | Practice / 🗣 | Schmidt |
| Exams | |||||
| ST 2026 | 7311619 | Hardware Synthesis and Optimisation | Becker | ||
| WT 26/27 | 7311619 | Hardware Synthesis and Optimisation | Becker | ||
The examination takes place within the framework of an oral overall examination (approx. 30 minutes).
The module grade is the grade of the oral exam.
none
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
Dr. Martin Rapp
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107448 - Hardware-efficient Artificial Intelligence |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400232 | Hardware-efficient Artificial Intelligence | 2 SWS | Lecture | Rapp |
| Exams | |||||
| WT 26/27 | 7500406 | Hardware-efficient Artificial Intelligence | Henkel | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) approx. 30 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 Abs. 3 SPO)
whether the examination takes place
None.
A basic understanding of deep learning concepts and neural networks is expected; familiarity with hardware architectures and embedded systems is helpful but not required.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106650 - HRI and Social Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400159 | HRI and Social Robotics | 4 SWS | Lecture / Practice / 🗣 | Bruno, Maure |
| Exams | |||||
| ST 2026 | 7500101 | HRI and Social Robotics | Bruno | ||
| WT 26/27 | 7500390 | HRI and Social Robotics - Post Exam | Bruno | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
None.
Knowledge of the content of modules Robotics I - Introduction to Robotics is helpful.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106650 - HRI and Social Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400159 | HRI and Social Robotics | 4 SWS | Lecture / Practice / 🗣 | Bruno, Maure |
| Exams | |||||
| ST 2026 | 7500358 | HRI and Social Robotics - Exercises | Bruno | ||
| WT 26/27 | 7500399 | HRI and Social Robotics - Pass (Post-Exam) | Bruno | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course. The assessment can only be repeated once.
Knowledge of the content of modules Robotics I - Introduction to Robotics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107166 - Human Computer Interaction |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24659 | Human-Computer-Interaction | 2 SWS | Lecture / 🧩 | Beigl |
| Exams | |||||
| ST 2026 | 7500048 | Human-Computer-Interaction | Beigl | ||
| ST 2026 | 7500390 | Human Computer Interaction | Riedel | ||
| WT 26/27 | 7500076 | Human-Computer-Interaction | Beigl | ||
The assessment is carried out as a written exam (of approx. 60 minutes) as a digital exam according to §2 (3) of the Statute for the implementation of online examinations. The exam takes place ON SITE at KIT!
Participation in the exercise is compulsory and the contents of the exercise are relevant for the examination.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107166 - Human Computer Interaction |
| Prerequisite for: |
T-INFO-114192 - Human-Computer-Interaction |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400095 | Human-Computer-Interaction | 1 SWS | Practice / 🧩 | Beigl |
| Exams | |||||
| ST 2026 | 7500121 | Human-Computer-Interaction Pass | Beigl | ||
| ST 2026 | 7500387 | Human Computer Interaction Pass | Riedel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Exercise sheets must be handed in regularly to pass the course. The specific details will be announced in the lecture.
None.
Participation in the exercise is compulsory and the contents of the exercise are relevant for the examination.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106649 - Humanoid Robots - Locomotion and Whole-Body Control |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400135 | Humanoid Robots – Locomotion and Whole-Body Control | 4 SWS | Lecture / 🗣 | Mombaur, Große Sundrup |
| Exams | |||||
| ST 2026 | 7500160 | Humanoid Robots - Locomotion and Whole-Body Control | Mombaur | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
The grade of the course is given based on the performance in in an individual programming project on the topic of humanoid robots, which consists of the definition and solution of the project itself as well as a subsequent oral presentation in a block event and the submission of a written report. Project work starts in the exercise slots during the second half of the term and ends during the lecture free time.
As a prerequisite for the enrollment in the project, the students must regularly and successfully participate in the exercises and present their results for the exercise sheets during the first part of the term, according to the modalities announced at the beginning of the course.
Both components can be completed in the same group of two students. Withdrawal is possible until 2 weeks after enrollment in the project.
Active participation in the class is expected from all students and is a necessary requirement for the course.
Attendance of the lectures Robotics I - Introduction to Robotics and Mechano-Informatics in Robotics is required.
Limitation to 30 participants
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106649 - Humanoid Robots - Locomotion and Whole-Body Control |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400135 | Humanoid Robots – Locomotion and Whole-Body Control | 4 SWS | Lecture / 🗣 | Mombaur, Große Sundrup |
| Exams | |||||
| ST 2026 | 7500364 | Humanoid Robots - Locomotion and Whole-Body Control -Pass | Mombaur | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO).
The grade of the course is given based on the performance in in an individual programming project on the topic of humanoid robots, which consists of the definition and solution of the project itself as well as a subsequent oral presentation in a block event and the submission of a written report. Project work starts in the exercise slots during the second half of the term and ends during the lecture free time.
As a prerequisite for the enrollment in the project, the students must regularly and successfully participate in the exercises and present their results for the exercise sheets during the first part of the term, according to the modalities announced at the beginning of the course.
Both components can be completed in the same group of two students. Withdrawal is possible until 2 weeks after enrollment in the project.
Active participation in the class is expected from all students and is a necessary requirement for the course.
Attendance of the lectures Robotics I - Introduction to Robotics and Mechano-Informatics in Robotics is required.
Limitation to 30 participants
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107152 - Humanoid Robots - Seminar |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400048 | Humanoid Robots - Seminar | 2 SWS | Seminar / 🗣 | Asfour |
| Exams | |||||
| WT 26/27 | 7500119 | Humanoid Robots - Seminar | Asfour | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). It includes a presentation at the end of the term and a term paper.
None.
Attending the lectures Robotics I – Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III – Sensors and Perception in Robotics, Mechano-Informatics and Robotics and Wearable Robotic Technologies is recommended.
| Coordinators: |
Dr.-Ing. Angelika Kaplan
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400094 | Ethics of IT | 2 SWS | Lecture / 🖥 | Reussner, Bagattini |
| Exams | |||||
| ST 2026 | 7500338 | Ethics in Informatics | Reussner | ||
| Coordinators: |
Dr.-Ing. Angelika Kaplan
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400094 | Ethics of IT | 2 SWS | Lecture / 🖥 | Reussner, Bagattini |
| Exams | |||||
| ST 2026 | 7500210 | Ethics in Informatics | Reussner | ||
| Coordinators: |
Prof. Dr.-Ing. Sören Hohmann
Prof. Dr. Werner Nahm
Prof. Dr.-Ing. Eric Sax
Prof. Dr. Wilhelm Stork
Prof. Dr.-Ing. Thomas Zwick
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-105073 - Student Innovation Lab |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2303192 | Innovation Lab | 2 SWS | Project / 🗣 | Hohmann, Zwick, Sax, Stork, Terzidis |
| WT 26/27 | 2303192 | Innovation Lab | 2 SWS | Project / 🗣 | Hohmann, Zwick, Sax, Stork, Nahm, Schmalen, Rost |
| Exams | |||||
| ST 2026 | 7303192 | Innovation Lab | Hohmann, Zwick, Stork, Sax, Nahm | ||
| WT 26/27 | 7303192 | Innovation Lab | Hohmann, Zwick, Stork, Sax, Nahm | ||
Success control takes place in the form of other types of examination and includes:
none
| Coordinators: |
Prof. Dr. Ann-Kristin Kupfer
Prof. Dr. Orestis Terzidis
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2572189 | International Entrepreneurship and Brand Development | 4 SWS | Block / 🗣 | Terzidis, Kupfer |
| Exams | |||||
| WT 26/27 | 7900156 | International Entrepreneurship and Brand Development | Kupfer, Terzidis | ||
Non exam assessment. The grade is based on the presentation, the subsequent discussion and the written elaboration.
Please contact the Marketing and Sales Research Group for further information.
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100800 - Internet of Everything |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424104 | Internet of Everything | 2 SWS | Lecture / 🗣 | Zitterbart, Hildenbrand, Mahrt, Neumeister |
| Exams | |||||
| ST 2026 | 7500071 | Internet of Everything | Zitterbart | ||
| ST 2026 | 7500397 | Internet of Everything for Retakers | Zitterbart | ||
| ST 2026 | 7500428 | Internet of Everything (Zweitwiederholung) | Zitterbart | ||
| WT 26/27 | 7500009 | Internet of Everything | Zitterbart | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
The contents of the lecture Introduction to Computer Networks are assumed to be known. Attendance of the lecture Telematics is strongly recommended, as the contents are an important basis for understanding and classifying the material.
Offered for the last time in WS 26/27!!!!!
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100749 - Introduction to Bioinformatics for Computer Scientists |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400055 | Introduction to Bioinformatics for Computer Scientists | 2 SWS | Lecture / 🧩 | Stamatakis |
| Exams | |||||
| ST 2026 | 7500330 | Introduction to Bioinformatics for Computer Scientists | Stamatakis | ||
| WT 26/27 | 7500057 | Introduction to Bioinformatics for Computer Scientists | Stamatakis | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Basic knowledge in the areas of theoretical computer science (algorithms, data structures) and technical computer science (sequential optimisation in C or C++, computer architectures, parallel programming, vector processors) will be beneficial.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106998 - IT Security |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400010 | IT Security | 4 SWS | Lecture / Practice / 🗣 | Müller-Quade, Wressnegger, Martin, Gharibyar |
| Exams | |||||
| ST 2026 | 7500025 | IT Security (Nachklausur) | Müller-Quade, Wressnegger, Strufe | ||
| WT 26/27 | 7500038 | IT Security | Müller-Quade, Strufe, Wressnegger | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
None.
Students should be familiar with the content of the compulsory lecture "Informationssicherheit".
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107176 - Lab Project: Speech Translation |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400150 | Lab Project: Speech Translation | 4 SWS | Practical course / 🗣 | Niehues, Dinh |
| Exams | |||||
| WT 26/27 | 7500350 | Lab Project: Speech Translation | Niehues | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
Students should have understood the theoretical principles as introduced in the lectures Deep Learning or Machine Translation.
| Coordinators: |
Dr. Eileen Kühn
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107821 - lntroduction to Quantum Machine Learning |
The assessment takes the form of a written examination (usually 60 minutes) in accordance with Section 4 (2) No. 1 SPO.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 Para. 3 SPO) whether the assessment will take place
None.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Humanities and Social Sciences KIT Department of Informatics |
| Part of: |
M-INFO-107663 - Localization of Mobile Agents |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24613 | Localization of Mobile Agents | 3 SWS | Lecture / Practice / 🧩 | Kim, Hanebeck, Frisch |
| Exams | |||||
| ST 2026 | 7500482 | Localization of Mobile Agents | Hanebeck | ||
The assessment is carried out as an oral examination, lasting 15-30 minutes in accordance with Section 4 (2) No. 2 SPO. Participants can contact the chair at any time to arrange an appointment for the oral examination. (§ 4 Abs. 2 Nr. 2 SPO)
T-INFO-115015 Digital exercise must be successfully completed before exam.
Knowledge of a higher-level programming language with mature libraries for scientific and numerical computing (e.g., Julia, Matlab, Python) is an advantage.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107663 - Localization of Mobile Agents |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24613 | Localization of Mobile Agents | 3 SWS | Lecture / Practice / 🧩 | Kim, Hanebeck, Frisch |
| Exams | |||||
| ST 2026 | 7500483 | Localization of Mobile Agents - Exercise | Hanebeck | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must complete a digital exercise in Ilias which is ungraded. The digital exercise can be repeated as often as desired.
None.
Knowledge of a higher-level programming language with mature libraries for scientific and numerical computing (e.g., Julia, Matlab, Python) is an advantage.
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107906 - Logic Programming |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400195 | Logic programming | 4 SWS | Lecture / Practice / 🧩 | Erdweg, Dormann |
| Exams | |||||
| WT 26/27 | 7500361 | Logic Programming | Erdweg | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
None.
| Coordinators: |
Prof. Dr. André Platzer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106102 - Logical Foundations of Cyber-Physical Systems |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400161 | Logical Foundations of Cyber-Physical Systems | 4 SWS | Lecture / 🗣 | Platzer |
| Exams | |||||
| WT 26/27 | 7500252 | Logical Foundations of Cyber-Physical Systems | Platzer | ||
The assessment is usually carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
In order to receive a bonus, you must earn at least 50% of the points for solving the exercises. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4).
None.
The course assumes prior exposure to basic computer programming and mathematical reasoning. This course covers the basic required mathematical and logical background of cyber-physical systems. You will be expected to follow the textbook as needed: André Platzer. Logical Foundations of Cyber-Physical Systems. Springer 2018. DOI:10.1007/978-3-319-63588-0
Course web page: https://lfcps.org/course/lfcps.html
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100807 - Low Power Design |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2424672 | Low Power Design | 2 SWS | Lecture / 🗣 | Henkel, Nassar, Khdr |
| Exams | |||||
| ST 2026 | 7500200 | VL: Low Power Design | Henkel | ||
| WT 26/27 | 7500139 | VL: Low Power Design | Henkel | ||
The assessment is carried out as an oral examination lasting 25-30 minutes, in accordance with Section 4 (2) No. 2 SPO.
None.
- Basic knowledge from the modules “Design and Architectures of Embedded Systems (ESII)” and “Optimization and Synthesis of Embedded Systems (ESI)” are helpful but not essential for understanding of this lecture.
- The lecture is equally suitable for students from both computer science as well as electrical engineering department.
- The Lab of “Low Power Design and Embedded Systems” enables students to apply some of the theoretical knowledge gained from the lecture in practice.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107169 - Machine Learning - Foundations and Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400018 | Machine Learning – Foundations and Algorithms | 4 SWS | Lecture / Practice / 🗣 | Neumann |
| Exams | |||||
| ST 2026 | 7500215 | Machine Learning - Foundations and Algorithms | Neumann | ||
| WT 26/27 | 7500292 | Machine Learning - Foundations and Algorithms | Neumann | ||
The success control takes place in the form of a written exam, usually 90 minutes in length, according to § 4 Abs. 2 Nr. 1 SPO.
A bonus can be acquired through successful participation in the exercise as a success control of a different kind (§4(2), 3 SPO 2008) or study performance (§4(3) SPO 2015). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4). The bonus is only valid for the main and post exams of the semester in which it was earned. After that, the grade bonus expires.
None.
- Attendance of the lecture “Foundations of Artificial Intelligence” (“Grundlagen der Künstlichen Intelligence”)
- Knowledge in python
- Mathematics-heavy lecture. The basics will be reviewed, but mathematical proficiency is helpful
| Coordinators: |
Prof. Dr. Pascal Friederich
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106959 - Machine Learning for Natural Sciences |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400008 | Machine Learning for the Natural Sciences | 2 SWS | Lecture / 🧩 | Friederich |
| Exams | |||||
| ST 2026 | 7500211 | Machine Learning for Natural Sciences | Friederich | ||
| ST 2026 | 7500426 | Machine Learning for Natural Sciences | Friederich | ||
Lecture: The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
Exercise: The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course. The assessment an only be repeated once.
None.
• Knowledge of the basics of machine learning is helpful but not required
• Interest in natural science topics is required
• Basic knowledge of python is recommended. It has to be acquired during the semester through self-study
| Coordinators: |
Prof. Dr. Pascal Friederich
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106959 - Machine Learning for Natural Sciences |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400034 | Exercise for Machine Learning for the Natural Sciences | 2 SWS | Lecture / Practice / 🧩 | Friederich, Torresi, Neubert, Eberhard, Schlöder, Metni, Teufel |
| Exams | |||||
| ST 2026 | 7500149 | Exercise for Machine Learning for the Natural Sciences | Friederich | ||
Lecture: The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
Exercise: The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course. The assessment an only be repeated once.
None.
• Knowledge of the basics of machine learning is helpful but not required
• Interest in natural science topics is required
• Basic knowledge of python is recommended. It has to be acquired during the semester through self-study
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106470 - Machine Learning in Climate and Environmental Sciences |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400003 | Machine Learning in Climate and Environmental Sciences | 4 SWS | Lecture / Practice / 🗣 | Nowack |
| Exams | |||||
| ST 2026 | 7500302 | Machine Learning in Climate and Environmental Sciences | Nowack | ||
The assessment of the lectures is likely carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60-120 minutes (exact duration to be confirmed).
Depending on the class size, this might be changed to an oral examination (lasting around 20 minutes, § 4 Abs. 2 No. 2 SPO). The exact type of assessment will be confirmed at least six weeks prior to the assessment.
No strict prerequisites but several strong recommendations (see below).
• Previous programming experience, e.g. in scientific contexts or in computer science, is required.
• Knowledge of fundamentals about machine learning is an advantage.
• Knowledge of the Python programming language is an advantage.
• Good knowledge of mathematical concepts such as linear algebra is an advantage.
• An interest in scientific questions important for the climate- and environmental sciences.
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106470 - Machine Learning in Climate and Environmental Sciences |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400003 | Machine Learning in Climate and Environmental Sciences | 4 SWS | Lecture / Practice / 🗣 | Nowack |
| Exams | |||||
| ST 2026 | 7500388 | Machine Learning in Climate and Environmental Sciences - Pass | Nowack | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course. The assessment an only be repeated once.
No strict prerequisites but several strong recommendations (see below).
• Previous programming experience, e.g. in scientific contexts or in computer science, is required.
• Knowledge of fundamentals about machine learning is an advantage.
• Knowledge of the Python programming language is an advantage.
• Good knowledge of mathematical concepts such as linear algebra is an advantage.
• An interest in scientific questions important for the climate- and environmental sciences.
| Coordinators: |
Prof. Dr. Martin Klarmann
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2571150 | Market Research | 2 SWS | Lecture / 🗣 | Klarmann |
| ST 2026 | 2571151 | Market Research Tutorial | 1 SWS | Practice / 🗣 | Klarmann |
| Exams | |||||
| ST 2026 | 7900015 | Market Research | Klarmann | ||
| WT 26/27 | 7900053 | Market Research | Klarmann | ||
The assessment of success takes place through a written exam (70 minutes) with additional aids in the sense of an open book exam. Further details will be announced during the lecture.
None
None
Please note that this course has to be completed successfully by students interested in master thesis positions at the Marketing & Sales Research Group.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106828 - Master's Thesis |
The Master's thesis is reglenented in § 14 and § 19 of the SPO23 Master of Computer Science. The presentation should take place no later than four weeks after submission of the Master's thesis.
The Master's thesis is assessed in the form of a report. An overall assessment (including the presentation) must be written.
The presentation should take place no later than four weeks after submission of the Master's thesis. The presentation may also take place before submission.
A prerequisite for admission to the Master's thesis is that students have generally already acquired 60 credit points, of which at least 15 credit points must come from one of the two specialization subjects. The application for admission to the Master's thesis must be submitted no later than three months after taking the last module examination.
This module component represents a final thesis. The following periods have been supplied:
| Submission deadline | 6 months |
|---|---|
| Maximum extension period | 3 months |
| Correction period | 8 weeks |
This thesis requires confirmation by the examination office.
| Coordinators: |
Prof. Dr. Ann-Kristin Kupfer
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2572192 | Media Management | 2 SWS | Lecture / 🗣 | Kupfer |
| WT 26/27 | 2572193 | Media Management Exercise | 1 SWS | Practice / 🗣 | Schmitt |
| Exams | |||||
| ST 2026 | 7900004 | Media Management | Kupfer | ||
| WT 26/27 | 7900135 | Media Management | Kupfer | ||
Success is assessed in the form of an examination of another type. The following aspects are included in the assessment:
Further details on the organization of the performance and the points system for the assessment will be announced in the lecture.
None
Students are highly encouraged to actively participate in class.
Please note that the lecture and exam will not be offered during the 2026/27 winter term.
| Coordinators: |
Prof. Dr. Oliver Waldhorst
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107245 - Mobile Communication |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400015 | Mobile Communication | 2 SWS | Lecture | Waldhorst, Mahrt |
| Exams | |||||
| ST 2026 | 7500073 | Mobile Communication | Waldhorst, Zitterbart | ||
| ST 2026 | 7500349 | Mobile Communication | Waldhorst, Zitterbart | ||
| WT 26/27 | 7500015 | Mobile Communication | Waldhorst, Zitterbart | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
The contents of the lecture Introduction to Computer Networks are assumed to be known. Attendance of the lecture Telematics is strongly recommended, as the contents are an important basis for understanding and classifying the material.
| Coordinators: |
Prof. Dr.-Ing. Peter Rost
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-105971 - Mobile Communications |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2310523 | Mobile Communications | 2 SWS | Lecture / 🗣 | Rost |
| WT 26/27 | 2310524 | Exercises for 2310523 Mobile Communications | 1 SWS | Practice / 🗣 | Rost |
| Exams | |||||
| ST 2026 | 7310524-1 | Mobile Communications | Rost | ||
| WT 26/27 | 7310524-1 | Mobile Communications | Rost | ||
The success control takes place in the form of an oral examination lasting approx. 25 minutes. Before the examination, there is a preparation phase of 15 minutes in which preparatory tasks are solved.
none
| Coordinators: |
Dr.-Ing. Erik Burger
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106931 - Model-Driven Software Development |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424657 | Model-Driven Software Development | 2 SWS | Lecture / 🗣 | Burger |
| Exams | |||||
| ST 2026 | 7500016 | Model Driven Software Development | Burger, Reussner | ||
| WT 26/27 | 7500086 | Model Driven Software Development | Reussner, Burger | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 25 minutes.
None.
Basic knowledge from the lecture Software Engineering II is helpful.
| Coordinators: |
Prof. Dr. Maria Aksenovich
|
|---|---|
| Organisation: |
KIT Department of Mathematics |
| Part of: |
M-MATH-106957 - Modern Methods in Combinatorics |
Oral examination (approx. 30 min)
None
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-102555 - Motion in Human and Machine - Seminar |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400063 | Motion in Human and Machine | 3 SWS | Seminar / 🗣 | Asfour |
| Exams | |||||
| ST 2026 | 7500272 | Motion in Man and Machine - seminar | Asfour | ||
| ST 2026 | 7500289 | Motion in Man and Machine - Seminar | Asfour | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). It includes a report of about two pages and a video presentation of about five minutes.
None.
Programming skills in C++, Python or Matlab are recommended.
Attendance of the lectures Robotics I – Introduction to Robotics, Robotics II – Humanoid Robotics, Robotics III – Sensors and Perception in Robotics, Mechano-Informatics and Robotics and Wearable Robot Technologies is recommended
This interdisciplinary seminar is conducted in cooperation with the University of Stuttgart and the University of Tübingen.
| Coordinators: |
Prof. Dr. Jan Niehues
Prof. Dr.-Ing. Rainer Stiefelhagen
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107676 - Multimodal Artificial Intelligence |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400141 | Multimodal Artificial Intelligence | 4 SWS | Lecture / 🗣 | Niehues, Stiefelhagen |
| Exams | |||||
| ST 2026 | 7500402 | Multimodal Artificial Intelligence | Niehues, Stiefelhagen | ||
| WT 26/27 | 7500210 | Multimodal Artificial Intelligence | Stiefelhagen | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) approx. 60 minutes.
None.
| Coordinators: |
Prof. Dr. Sebastian Kempf
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-105604 - Nano- and Quantum Electronics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2312668 | Nano- and Quantum Electronics | 3 SWS | Lecture / 🗣 | Kempf |
| ST 2026 | 2312670 | Tutorial for 2312668 Nano- and Quantum Electronics | 1 SWS | Practice / 🗣 | Kempf, Mitarbeiter*innen |
| Exams | |||||
| ST 2026 | 7312668 | Nano- and Quantum Electronics | Kempf | ||
| WT 26/27 | 7312668 | Nano- and Quantum Electronics | Kempf | ||
The assessment of success takes place in the form of a written examination lasting 120min. The grade corresponds to the result of the written examination.
none
Successful completion of the modules "Superconductivity for Engineers" and „Einführung in die Quantentheorie für Elektrotechniker“ is recommended.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107178 - Natural Language Processing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400147 | Natural Language Processing | 4 SWS | Lecture / 🗣 | Niehues, Liu, Züfle |
| Exams | |||||
| ST 2026 | 7500223 | Natural Language Processing | Niehues | ||
| WT 26/27 | 7500286 | Natural Language Processing | Niehues | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
None.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107233 - Natural Language Processing and Software Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424187 | Natural Language Processing and Software Engineering | 2 SWS | Lecture / 🗣 | Hey, Koziolek, Keim |
| Exams | |||||
| ST 2026 | 7500185 | Natural Language Processing and Software Engineering | Koziolek, Hey | ||
| ST 2026 | 7543231 | Natural Language Processing and Software Engineering | Hey, Keim | ||
| WT 26/27 | 7543231 | Natural Language Processing and Software Engineering | Hey, Keim | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 25 minutes.
None.
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107218 - Network Security: Architectures and Protocols |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24601 | Network Security: Architectures and Protocols | 2 SWS | Lecture / 🗣 | Baumgart, Bless, Zitterbart |
| Exams | |||||
| ST 2026 | 7500040 | Network Security: Architectures and Protocols | Zitterbart, Bless, Baumgart | ||
| ST 2026 | 7500072 | Network Security: Architectures and Protocols | Zitterbart, Bless, Baumgart | ||
| ST 2026 | 7500429 | Network Security: Architectures and Protocols for Retakers | Zitterbart, Bless, Baumgart | ||
| WT 26/27 | 7500014 | Network Security: Architectures and Protocols | Zitterbart, Bless, Baumgart | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
The contents of the lecture Introduction to Computer Networks are assumed to be known. Attendance of the lecture Telematics is strongly recommended, as the contents are an important basis for understanding and classifying the material.
| Coordinators: |
Dr.-Ing. Roland Bless
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100784 - Next Generation Internet |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24674 | Next Generation Internet | 2 SWS | Lecture / 🗣 | Bless |
| Exams | |||||
| ST 2026 | 7500074 | Next Generation Internet | Bless, Zitterbart | ||
| ST 2026 | 7500427 | Next Generation Internet for RETAKERS | Bless, Zitterbart | ||
| WT 26/27 | 7500016 | Next Generation Internet | Bless, Zitterbart | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
The contents of the lecture Introduction to Computer Networks are assumed to be known. Attendance of the lecture Telematics is strongly recommended, as the contents are an important basis for understanding and classifying the material.
| Coordinators: |
Prof. Dr. Ann-Kristin Kupfer
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-106258 - Digital Marketing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2571184 | Online concepts for Karlsruhe city retailers | 2 SWS | Others / 🗣 | Kupfer |
| Exams | |||||
| ST 2026 | 7900221 | Online Concepts for Karlsruhe City Retailers | Kupfer | ||
Alternative exam assessment:
Please note that an application is required to participate in this workshop. The application phase usually takes place at the beginning of the lecture period in the summer semester. More information on the application process is usually available on the Marketing and Sales Research Group website (marketing.iism.kit.edu) shortly before the start of the lecture period in the summer semester.
| Coordinators: |
Prof. Dr. Wilhelm Stork
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-100456 - Optical Engineering |
| Exams | |||||
|---|---|---|---|---|---|
| WT 26/27 | 7311629 | Optical Engineering | Stork | ||
Achievement will be examined in an oral examination (approx. 20 minutes)
none
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107229 - Optimization and Synthesis of Embedded Systems (ESI) |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500038 | VL: Optimization and synthesis of embedded systems (ES1) | Henkel | ||
| WT 26/27 | 7500085 | VL: Optimization and synthesis of embedded systems (ES1) | Henkel | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting approx. 20 minutes.
None.
Knowledge of computer structures is helpful.
The prerequisites, if any, are explained in more detail in the module description.
| Coordinators: |
Prof. Dr.-Ing. Jürgen Beyerer
Julius Pfrommer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105329 - Optimization Methods for Machine Learning and Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400280 | Optimization Methods for Machine Learning and Engineering | 2 SWS | Lecture / 🧩 | Pfrommer, Beyerer |
| WT 26/27 | 2400281 | Optimization Methods for Machine Learning and Engineering | 1 SWS | Practice / 🧩 | Pfrommer, Beyerer |
| Exams | |||||
| ST 2026 | 7500329 | Optimization Methods for Machine Learning and Engineering | Beyerer | ||
| WT 26/27 | 7500279 | Optimization Methods for Machine Learning and Engineering | Beyerer | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107199 - Parallel Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400053 | Parallel Algorithms | 3 SWS | Lecture / 🗣 | Sanders, Uhl, Laupichler |
| Exams | |||||
| ST 2026 | 7500378 | Parallel Algorithms | Sanders | ||
| WT 26/27 | 7575489 | Parallel Algorithms | Sanders | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge from lectures such as Algorithms I/II is recommended.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107199 - Parallel Algorithms |
| Exams | |||||
|---|---|---|---|---|---|
| WT 26/27 | 7500163 | Parallel Algorithms Pass | Sanders | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course.
None.
Knowledge from lectures such as Algorithms I/II is recommended.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107167 - Parameterized Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400178 | Parameterized Algorithms | 4 SWS | Lecture / Practice / 🗣 | Bläsius |
| Exams | |||||
| ST 2026 | 7500421 | Parameterized Algorithms | Bläsius | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Basic knowledge of algorithms and data structures (e.g. from the lectures Algorithms 1 + 2) is helpful.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107167 - Parameterized Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400178 | Parameterized Algorithms | 4 SWS | Lecture / Practice / 🗣 | Bläsius |
| Exams | |||||
| ST 2026 | 7500421 | Parameterized Algorithms | Bläsius | ||
| ST 2026 | 7500422 | Parameterized Algorithms - Pass | Bläsius | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A total of two repetitions are possible.
None.
Basic knowledge of algorithms and data structures (e.g. from the lectures Algorithms 1 + 2) is helpful.
| Coordinators: |
Prof. Dr. Kathrin Gerling
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107170 - Participatory Technology Design |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400189 | Participatory Technology Design (V/Ü) | 4 SWS | Lecture / Practice | Gerling |
| Exams | |||||
| ST 2026 | 7500375 | Participatory Technology Design (Re-Exam) | Gerling | ||
| WT 26/27 | 7500307 | Participatory Technology Design (Exam) | Gerling | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
None.
Knowledge of the basics of human-machine interaction is helpful.
| Coordinators: |
Prof. Dr. Kathrin Gerling
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107170 - Participatory Technology Design |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400189 | Participatory Technology Design (V/Ü) | 4 SWS | Lecture / Practice | Gerling |
| Exams | |||||
| WT 26/27 | 7500278 | Participatory Technology Design (Exercise) | Gerling | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). A total of two repetitions are possible.
None.
Knowledge of the basics of human-machine interaction is helpful.
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107817 - Photorealistic Rendering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400180 | Photorealistic Image Synthesis | 2 SWS | Lecture / 🗣 | Schudeiske, Dachsbacher |
| Exams | |||||
| WT 26/27 | 7500203 | Fotorealistic Rendering | Dachsbacher | ||
Assessment takes the form of an oral examination, generally lasting 25 minutes, in accordance with § 4 (2) No. 2 of the Study Regulations.
None.
Recommendation: the Computergraphik lecture (24081).
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107642 - Post-quantum Cryptography from Lattices |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400076 | Post-quantum Cryptography from Lattices | 2 SWS | Lecture / 🗣 | Klooß, Kühn, Brede |
| Exams | |||||
| ST 2026 | 7500380 | Post-quantum Cryptography from Lattices | Klooß | ||
| WT 26/27 | 7500380 | Post-quantum Cryptography from Lattices | Klooß | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) approx. 45 minutes.
None.
Basic familiarity with cryptography as introduced in the module “Theoretische Grundlagen der Kryptographie” is recommended, in particular, familiarity with security notions for basic primitives (IND-CPA, EUF-CMA) and security reductions (including hybrid arguments and guessing).
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107739 - Practical Course: Advanced Software Development Tools |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500062 | Practical Course: Advanced Software Development Tools | Schaefer | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper (or implementation and documentation) must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
The course can also be taken in German by arrangement.
| Coordinators: |
Prof. Dr. Achim Streit
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105870 - Practical Course: Advanced Topics in High Performance Computing, Data Management and Analytics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400068 | Advanced Topics in High Performance Computing, Data Management and Analytics | 3 SWS | Practical course | Streit, Farhadi |
| WT 26/27 | 2400043 | Advanced Topics in High Performance Computing, Data Management and Analytics | 3 SWS | Practical course | Farhadi, Streit |
| Exams | |||||
| ST 2026 | 7500269 | Practical Course: Advanced Topics in High Performance Computing, Data Management and Analytics | Streit | ||
| WT 26/27 | 7500345 | Practical Course: Advanced Topics in High Performance Computing, Data Management and Analytics | Streit | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The examination can consist of experiments or projects, each with a concluding presentation. Students may redraw from the assigned topic during the first two weeks after the topic has been communicated.
None.
Knowledge in the area of databases, data management, data analytics, parallel computing is helpful.
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106800 - Practical Course: AI for Climate and Weather Predictions |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400082 | AI for climate and weather predictions | 3 SWS | Practical course / 🗣 | Nowack, Amiramjadi |
| WT 26/27 | 2400064 | AI for climate and weather predictions | 2 SWS | Practical course / 🗣 | Nowack, Amiramjadi |
| Exams | |||||
| ST 2026 | 7500385 | Practical Course: AI for Climate and Weather Predictions | Nowack | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
• Previous programming experience, e.g., in scientific contexts or in computer science, is required.
• Students should have previous experience in the theory and implementation of machine learning models.
• Knowledge of the Python programming language.
• Good knowledge of mathematical concepts such as linear algebra is an advantage.
• An interest in scientific questions around climate science and weather forecasting.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106996 - Practical Course: Application Security |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400117 | Application Security Lab | 4 SWS | Practical course / 🗣 | Müller-Quade, Wressnegger, Sperrle, Gharibyar |
| WT 26/27 | 2400114 | Application Security Lab | 4 SWS | Practical course / 🗣 | Müller-Quade, Mechler, Noppel, Wressnegger, Gharibyar |
| Exams | |||||
| ST 2026 | 7500119 | Application Security Lab | Müller-Quade, Wressnegger | ||
| WT 26/27 | 7500188 | Application Security Lab | Müller-Quade, Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). Students have to solve different tasks. An overall grade is awarded.
None.
The basics of IT security are assumed.
The content of the lectures "Computer Organization" and "Operating Systems" should be known.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106867 - Practical Course: Artificial Intelligence & Security Lab (AISEC-Lab) |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24002 | Practical Course: Artificial Intelligence & Security (AISEC-Lab) | 4 SWS | Practical course / 🗣 | Wressnegger, Zhao |
| Exams | |||||
| ST 2026 | 7500096 | Practical Course: Artificial Intelligence & Security (AISEC-Lab) | Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
At least one assignment from each unit must be successfully completed (comparable results to other students).
None.
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Daniel Schadt
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107412 - Practical Course: Building PETs |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400056 | Practical Course: Building PETs | 4 SWS | Practical course / 🗣 | Strufe |
| Exams | |||||
| WT 26/27 | 7500270 | Practical Course: Building PETs | Strufe | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
The students must hand in the source code of their programs to be examined. Additionally, each student must explain their solutions to the tasks. An overall grade is awarded.
None.
Familiarity with a programming language and a HTTP library is recommended.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107265 - Practical Course: Chip Design I |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400104 | Practical Course: Chip Design I | 4 SWS | Practical course / 🗣 | Tahoori |
| WT 26/27 | 2400216 | Practical Course: Chip Design I | 4 SWS | Practical course / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500234 | Practical Course: Chip Design I | Tahoori | ||
| WT 26/27 | 7500192 | Practical Course: Chip Design I | Tahoori | ||
The assessment is carried out as an examination of another type (§ 4 Abs.
2 No. 3 SPO).
The overall impression is evaluated. The grading is based on the results of the practical work (80 %) and the final presentation(20%). An overall grade is awarded.
Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
None.
The requirements are individual to each of the offered projects.
Knowledge of HDL is helpful.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107266 - Practical Course: Chip Design II |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400107 | Practical Course: Chip Design II | 4 SWS | Practical course / 🗣 | Tahoori |
| WT 26/27 | 2400219 | Practical Course: Chip Design II | 4 SWS | Practical course / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500169 | Practical Course: Chip Design II | Tahoori | ||
| WT 26/27 | 7500220 | Practical Course: Chip Design II | Tahoori | ||
The assessment is carried out as an examination of another type (§ 4 Abs.
2 No. 3 SPO).
The overall impression is evaluated. The grading is based on the results of the practical work (80 %) and the final presentation(20%). An overall grade is awarded.
Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
None.
The requirements are individual to each of the offered projects.
Knowledge of HDL is helpful.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107677 - Practical Course: Continuous Software Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400105 | Practical Course: Continuous Software Engineering | Practical course | Koziolek | |
| Exams | |||||
| ST 2026 | 7500243 | Practical Course: Continuous Software Engineering | Koziolek | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of predominantly practical tasks and a final presentation.
None.
Solid programming skills are required in order to successfully complete the lab course within the specified workload.
In earlier semesters, this course was named „Praktikum: Werkzeuge für agile Modellierung “.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107897 - Practical Course: Customized Embedded Processor Design |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500203 | Lab: Customized Embedded Processor Design | Henkel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of a practical assignment, presentations and, if applicable, a written paper. The written paper, presentations and practical work are weighted depending on the course.
None.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107919 - Practical Course: Designing and Conducting Experimental Studies |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 240007 | Praktical Course: Designing and Conducting Experimental Studies | 4 SWS | Practical course / 🗣 | Beigl, Riedel, Ramesh, Zitz, Breitling |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall impression is evaluated. The following partial aspects are included in the grading: Term paper (approx. 20 pages), Presentation (duration approx. 20 min.), Practical training (protocols)
The grading scale will be announced in the course. Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
Recommendation: The student should be familiar with the content of the course “Human Computer Interaction” or similar.
Nach Rücksprache kann der Leistungsnachweis auch auf Deutsch erbracht werden.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107678 - Practical Course: Engineering Approaches to Software Development |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24908 | Practical Course Engineering Approaches to Software Development | 4 SWS | Practical course / 🗣 | Reussner |
| WT 26/27 | 2400093 | Practical Course: Engineering Approaches to Software Development | 4 SWS | Practical course | Reussner |
| Exams | |||||
| ST 2026 | 7500184 | Practical Course Engineering Approaches to Software Development | Burger, Reussner | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of predominantly practical tasks and a final presentation.
None.
Solid programming skills are required in order to successfully complete the lab course within the specified workload.
In earlier semesters, this course was named „Praktikum Ingenieursmäßige Software-Entwicklung “.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106784 - Practical Course: Fine-grained Algorithm Design and Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400104 | Fine-grained Algorithm Design and Engineering | 4 SWS | Practical course / 🗣 | Künnemann-Goos, Angrick, Gokaj |
| Exams | |||||
| WT 26/27 | 7500042 | Fine-Grained Algorithm Design and Engineering | Künnemann-Goos | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall performance is evaluated, which includes the quality of the produced results, the project report and the presentation.
None.
- Basic knowledge of algorithms and data structures is assumed.
- Knowledge of fine-grained complexity is helpful, but not required.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-102661 - Practical Course: FPGA Programming |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400106 | FPGA Programming | 4 SWS | Practical course / 🗣 | Tahoori |
| WT 26/27 | 2400106 | FPGA Programming | 4 SWS | Practical course / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500087 | Practical Course FPGA Programming | Tahoori | ||
| WT 26/27 | 7500083 | Practical Course FPGA Programming | Tahoori | ||
The assessment is carried out in form of an examination of another type (§ 4 Abs. 2 No. 3 SPO). Students must give a presentation.
The module grade is made up of 80% of the work completed in the practical course and 20% of the presentation.
An overall grade is awarded.
None.
Knowledge of “Dependable Computing” and “Fault Tolerant Computing” and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100724 - Practical Course: General-Purpose Computation on Graphics Processing Units |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24911 | Practical course General-Purpose Computation on Graphics Processing Units | 2 SWS | Practical course / 🗣 | Lerzer, Klepikov, Dachsbacher, Dereviannykh |
| WT 26/27 | 2424297 | Practical course General-Purpose Computation on Graphics Processing Units | 2 SWS | Practical course / 🗣 | Klepikov, Lerzer, Dachsbacher |
| Exams | |||||
| ST 2026 | 7500134 | Practical-course General-Purpose Computation on Graphics Processing Units | Dachsbacher | ||
| WT 26/27 | 7500470 | Practical Course: General-Purpose Computation on Graphics Processing Units | Dachsbacher | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Performance is assessed continuously for the individual projects and in a final presentation.
None.
It is recommended to have attended relevant lectures in the specialisation area of computer graphics.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106646 - Practical Course: Human-Centred Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400149 | Practical course: Human-Centred Robotics | 4 SWS | Practical course / 🗣 | Mombaur, Lee |
| WT 26/27 | 2400193 | Practical Course: Human-Centred Robotics Projects | 4 SWS | Practical course / 🗣 | Mombaur, Lee |
| Exams | |||||
| ST 2026 | 7500168 | Practical Course: Human-Centred Robotics | Mombaur | ||
| WT 26/27 | 7500387 | Practical Course: Human-Centred Robotics | Mombaur | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
This includes the preparation of a project report (ca. 10 pages and an oral presentation with slides and hardware demonstration (30 Min + 15 min questions). Students may withdraw from the examination during the first two weeks after the topic has been communicated.
Programming skills are required.
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) are very helpful.
Limited number of projects and participants. Specific project topics will be different each term and will be announced in a presentation during the first semester week.
| Coordinators: |
Prof. Dr. Bernhard Beckert
Terru Stübinger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107494 - Practical Course: Interactive Theorem Proving |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400216 | Interactive Theorem Proving | 2 SWS | Practical course / 🗣 | Erdweg, Xu, Lego |
| WT 26/27 | 2400229 | "Practical Course: Interactive Theorem Proving" | 2 SWS | Practical course / 🗣 | Beckert, Stübinger, Färber |
| Exams | |||||
| ST 2026 | 7500320 | Practical Course: Interactive Theorem Proving | Erdweg | ||
| WT 26/27 | 7500297 | Practical Course: Interactive Theorem Proving | Beckert | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). Students are graded on the formalisation (e.g. Isabelle theory files) produced during the project phase, along with a presentation on their completed project (duration approx. 20min). Students may redraw from the examination until two weeks before the final presentations. The assessment can be repeated once.
None.
- Knowledge of functional programming is assumed.
- Some knowledge of logic (e.g. via courses such as Formale Systeme and/or Constructive Logic) will be helpful, but is not required
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-103706 - Practical Course: Internet of Things (IoT) |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2424304 | Internet of Things (IoT) Lab | 4 SWS | Practical course / 🖥 | Henkel, Mentzos, Tobar |
| WT 26/27 | 2424304 | Internet of Things (IoT) Lab | 4 SWS | Practical course / 🗣 | Mentzos, Henkel, Tobar |
| Exams | |||||
| ST 2026 | 7500187 | Lab: Internet of Things (IoT) | Henkel | ||
| WT 26/27 | 7500183 | Lab: Internet of Things (IoT) | Henkel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of a practical assignment, presentations and, if necessary, a written report. Written reports, presentations and practical work are weighted depending on the event.
Basic skills in C or C++ programming.
- Familiarity with other (than C) languages like Python could be helpful as well.
- Basic knowledge from the modules “Design and Architectures of Embedded Systems (ESII)” and “Optimization and Synthesis of Embedded Systems (ESI)” are helpful but not essential for understanding the lab.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-104031 - Practical Course: Low Power Design and Embedded Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2424811 | Low Power Design and Embedded Systems | 4 SWS | Practical course / 🧩 | Henkel, Khdr, Sikal, Mentzos |
| Exams | |||||
| ST 2026 | 7500158 | Lab: Low Power Design and Embedded Systems | Henkel | ||
| WT 26/27 | 7500104 | Lab: Low Power Design and Embedded Systems | Henkel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
The overall impression is evaluated.
The grading will be based on multiple exercises and a final report.
Details of the grading scale will be announced during the course.
None.
Students should be familiar with software development practices under Linux-based systems. Practical knowledge in C/C++ as well as Python is required.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107845 - Practical Course: Machine Learning and Intelligent Systems |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424281 | Practical Course Machine Learning and Intelligent Systems | 4 SWS | Practical course / 🗣 | Hanebeck |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall assessment consists of a written report, three presentations, and an assessment of the scientific work and implementation work carried out during the course.
None.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107609 - Practical Course: Mathematical and Computational Methods in Robotics and AI |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400202 | Practical Course: Mathematical and Computational Methods in Robotics & AI | 4 SWS | Practical course / 🗣 | Mombaur, Große Sundrup |
| WT 26/27 | 2400181 | Practical Course: Mathematical and Computational Methods in Robotics & AI | 4 SWS | Practical course / 🗣 | Mombaur, Große Sundrup |
| Exams | |||||
| ST 2026 | 7500306 | Practical Course: Mathematical and Computational Methods in Robotics and AI | Mombaur | ||
| WT 26/27 | 7500388 | Practical Course: Mathematical and Computational Methods in Robotics and AI | Mombaur | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
This includes the preparation of a project report (ca. 10 pages and an oral presentation (30 Min + 15 min questions). Students may withdraw from the examination during the first two weeks after the topic has been communicated.
Programming skills
Basic mathematical knowledge will be helpful, depending on topic. For Projects in the context of robotics, knowledge of robotics is recommended, but not strictly necessary.
Limited number of projects and participants. Specific project topics will be different each term and will be announced in a presentation during the first semester week or can be indivdually agreed upon with people supervising the practical. Students can propose topics on their own as well.
| Coordinators: |
Dr.-Ing. Erik Burger
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106932 - Practical Course: Model-Driven Software Development |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400091 | Practical Course: Model-Driven Software Development | 4 SWS | Practical course / 🗣 | Burger |
| Exams | |||||
| ST 2026 | 7500017 | Practical Course: Model-Driven Software Development | Reussner | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), in the form of predominantly practical tasks.
None.
Attending the lectures Software Engineering II and Model-Driven Software Development is helpful.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106648 - Practical Course: Movement and Technology |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400151 | Practical Course: Movement and Technology | 4 SWS | Practical course / 🗣 | Mombaur, Lau |
| Exams | |||||
| ST 2026 | 7500171 | Practical Course: Movement and Technology | Mombaur | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
This includes the preparation of a project report (ca. 10 pages and an oral presentation of the project topics and results with slides. Students may withdraw from the examination during the first two weeks after the topic has been communicated.
Programming skills are required.
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) are very helpful.
Programming skills.
Limited number of projects and participants. Specific project topics will be different each term and will be announced in a presentation during the first semester week.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107177 - Practical Course: Natural Language Dialog Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400187 | Praktikum Natural Language Dialog Systems | 2 SWS | Practical course / 🗣 | Niehues, Koneru |
| Exams | |||||
| ST 2026 | 7500371 | Practical Course Natural Language Dialog Systems | Niehues | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
| Coordinators: |
Dr.-Ing. Dennis Gnad
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106503 - Practical Course: Neural Network Acceleration on FPGAs |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400205 | Neural Network Acceleration on FPGAs | 4 SWS | Practical course / 🗣 | Tahoori |
| WT 26/27 | 2400205 | Practical Course: Neural Network Acceleration on FPGAs | 4 SWS | Practical course / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500054 | Practical Course: Neural Network Acceleration on FPGAs | Tahoori | ||
| WT 26/27 | 7500222 | Practical Course: Neural Network Acceleration on FPGAs | Tahoori | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A presentation must be given.
The module grade is composed of 80 % of the performance in the internship and 20 % of the presentation.
None.
Elementary knowledge in hardware design and/or neural networks are required.
Intermediate or advanced knowledge in at least one of the aspects is recommended.
| Coordinators: |
Dr. Ragnar Groot Koerkamp
Nikolai Maas
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107865 - Practical Course: Performance Engineering in C++ and Rust |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400204 | Practical Course: Performance Engineering in C++ and Rust | 4 SWS | Practical course / 🧩 | Sanders, Williams, Groot Koerkamp, Borowitz, Maas |
The assessment is carried out as an examination of another type ( 4 Abs. 2 No. 3 SPO). Students have to solve multiple programming tasks in C++ or Rust. An overall grade is awarded.
None.
At least basic knowledge of the C++ language *or* Rust language is necessary for
participation in the course. Students should be able to implement given algorithms.
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-108020 - Practical Course: Programming Language Design and Implementation |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400248 | Programming Language Design and Implementation | 2 SWS | Practical course / 🗣 | Erdweg |
| Exams | |||||
| WT 26/27 | 7500294 | Practical Course: Programming Language Design and Implementation | Erdweg | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). Students are graded on the developed language implementation during the project phase, along with a presentation on their completed project (duration approx. 20min). Students may withdraw from the examination until two weeks after the projects have been assigned. The assessment can be repeated once.
None.
Very good programming skills.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106627 - Practical Course: Real-world Vulnerability Discovery and Exploits |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 241337 | Practical Course: Real-world Vulnerability Discovery and Exploits | 2 SWS | Practical course / 🗣 | Wressnegger |
| WT 26/27 | 241337 | Practical Course: Real-world Vulnerability Discovery and Exploits | 4 SWS | Practical course / 🗣 | Wressnegger |
| Exams | |||||
| ST 2026 | 7500376 | Practical Course: Real-world Vulnerability Discovery and Exploits | Wressnegger | ||
| WT 26/27 | 7500383 | Practical Course: Real-world Vulnerability Discovery and Exploits | Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
Application security internship
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107155 - Robotics - Practical Course |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24870 | Robotics - Practical Course | 4 SWS | Practical course / 🗣 | Asfour |
| Exams | |||||
| ST 2026 | 7500261 | Robotics - Practical Course | Asfour | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). It is composed of several sub-tasks.
Knowledge of the programming language C++ is required.
Attending the lectures Robotics I – Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics and Mechano-Informatics and Robotics is recommended.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107600 - Practical Course: Route Planning |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400252 | Practical Course: Route Planning | 4 SWS | Practical course / 🗣 | Bläsius, Zündorf, Feilhauer, Steil |
| WT 26/27 | 2424305 | Practical Course in Algorithm Design | 4 SWS | Practical course / 🗣 | Feilhauer, Bläsius, Zündorf |
| Exams | |||||
| WT 26/27 | 7500072 | Practical Course in Algorithm Design | Ueckerdt | ||
| WT 26/27 | 7500314 | Practical Course: Route Planning | Bläsius | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO)
The grade is made up of the completion of the programming task, a written evaluation of the results (approx. 10 pages) and the final presentation.
None.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105453 - Practical Course: Security, Usability and Society |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2512554 | Practical Course: Security, Usability and Society | 3 SWS | Practical course / 🧩 | Volkamer, Strufe, Hennig, Länge, Fallahi, Hilt, Reimer, Algedri, Mack |
| WT 26/27 | 2512554 | Praktikum Security, Usability and Society (Bachelor) | 3 SWS | Practical course / 🧩 | Volkamer, Strufe, Berens, Fallahi, Ballreich, Hennig, Länge, Hilt, Algedri, Mack, Matheis, Reimer |
| WT 26/27 | 2512555 | Praktikum Security, Usability and Society (Master) | 3 SWS | Practical course / 🧩 | Volkamer, Strufe, Berens, Fallahi, Ballreich, Hennig, Länge, Hilt, Mack, Matheis, Algedri, Reimer |
| Exams | |||||
| ST 2026 | 7500322 | Practical Course: Security, Usability and Society | Strufe | ||
| ST 2026 | 7900029 | Practical lab Security, Usability and Society (Bachelor) | Volkamer, Strufe | ||
| ST 2026 | 7900178 | Practical Lab Security, Usability and Society (Master) | Volkamer, Strufe | ||
| WT 26/27 | 7500304 | Practical Course: Security, Usability and Society | Strufe | ||
| WT 26/27 | 7900116 | Advanced Lab Security, Usability and Society (Bachelor) | Volkamer | ||
| WT 26/27 | 7900307 | Advanced Lab Security, Usability and Society (Master) | Volkamer | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
| Coordinators: |
Dr.-Ing. Richard Jumar
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105955 - Practical Course: Smart Energy System |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400170 | Laboratory: Smart Energy System Lab | 4 SWS | Practical course / 🗣 | Hagenmeyer, Waczowicz, Jumar, Aghaei Attar |
| WT 26/27 | 2400230 | Lab Course: Smart Energy System Lab | 4 SWS | Practical course / 🗣 | Hagenmeyer, Jumar, Aghaei Attar |
| Exams | |||||
| ST 2026 | 7500318 | Practical Course: Smart Energy System Lab | Hagenmeyer | ||
| WT 26/27 | 7500318 | Practical Course: Smart Energy System Lab | Hagenmeyer | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
An overall grade is awarded which consists of:
40% 10 min presentation (per team) + 5 min of discussion
60% Write up in the style of a research paper of around 5-6 pages (for solo participants) or 7-8 pages (for teams of two)
The students may redraw from the examination before the end of the first laboratory week. The assessment
can be repeated once.
None.
- Knowledge of the fundamentals of energy informatics is a prerequisite.
- Knowledge of the fundamentals of electrical engineering and energy technology is required.
- Knowledge of the basics of mechatronics, data analysis and signal processing is helpful.
- Knowledge of power systems or power electronics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-106904 - Practical Course: Software Development and Application of Mobile, Bio-Inspired Robots |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2121342 | Practical Course: Software Development and Application of Mobile, Bio-Inspired Robots | 4 SWS | Practical course / 🗣 | Rönnau |
| Exams | |||||
| WT 26/27 | 76-T-MACH-113854 | Practical Course: Software Development and Application of Mobile, Bio-Inspired Robots | Rönnau | ||
Success is assessed in the form of a different type of examination in accordance with Section 4 (2) No. 3 SPO.
Code must be generated, a written report prepared and a presentation given.
None
Knowledge of the basics of robotics from Robotics 1 is helpful. Basic knowledge of C++ or Python and Linux is a prerequisite.
The course is offered in English.
| Coordinators: |
Prof. Dr.-Ing. Carsten Dachsbacher
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-101567 - Practical Course: Visual Computing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2424902 | Practical course GPU-Computing | 4 SWS | Practical course / 🗣 | Lerzer, Klepikov, Dereviannykh, Dachsbacher |
| WT 26/27 | 2424283 | Practical course GPU-Computing | 4 SWS | Practical course / 🗣 | Klepikov, Lerzer, Dachsbacher |
| Exams | |||||
| ST 2026 | 7500125 | Practical Course GPU-Computing | Dachsbacher | ||
| WT 26/27 | 7500110 | Practical Course GPU-Computing | Dachsbacher | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO) in the form of practical work, presentations and, if applicable, a written paper
Written papers, presentations and practical work are weighted according to the course.
None.
Programming skills in C/C++ are recommended.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107241 - Practical Introduction to Hardware Security |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400009 | Practical Introduction in Hardware Security | 4 SWS | Lecture / Practice / 🗣 | Tahoori, Gnad |
| WT 26/27 | 2400033 | Practical Introduction to Hardware Security | 4 SWS | Lecture / Practice / 🗣 | Gnad, Tahoori |
| Exams | |||||
| ST 2026 | 7500224 | Practical Introduction to Hardware Security | Tahoori | ||
| WT 26/27 | 7500226 | Practical Introduction to Hardware Security | Tahoori | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). 4 topics will be covered in this lecture. After each topic the student will receive an assignment. The quality of his tasks will be evaluated afterwards of its correctness.
None.
Knowledge of Digital Design (lecture TI)
Practical Course “FPGA Programming”
| Coordinators: |
Dr. Tomas Balyo
Dr. Ashlin Iser
Prof. Dr. Peter Sanders
Dr. Dominik Schreiber
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107238 - Practical SAT Solving |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400218 | Practical SAT Solving | 3 SWS | Lecture / Practice | Sanders, Iser, Schreiber |
| Exams | |||||
| ST 2026 | 7500415 | Practical SAT Solving | Sanders | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) usually lasting 30 minutes.
None.
Relevant literature will be announced in the lecture.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107829 - Practice of Research (Methods, 1st Semester) |
Progress is assessed through the following examination components:
◦ written assignments,
◦ short presentations,
◦ discussions on the course content.
The number and content of the required assignments will be announced at the beginning of the semester.
None.
Registration for this module is only possible in conjunction with registration for the module “Practice of Research (Project, 1st Semester)”; see there.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107830 - Practice of Research (Methods, 2nd Semester) |
Progress is assessed through the following examination components:
◦ written assignments,
◦ short presentations,
◦ discussions on the course content.
The number and content of the required assignments will be announced at the beginning of the semester.
None.
Registration for this module is only possible in conjunction with registration for the module “Practice of Research (Methods, 1st Semester)”; see there.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107831 - Practice of Research (Project, 1st Semester) |
Progress assessments take the form of the following examination components
- Other types of progress assessments (§4 Abs. 2 Nr. 2 of the SPO) in the form of work to be completed during the semester, namely
◦ written assignments,
◦ project presentations,
◦ a discussion of the project’s content,
◦ the completion of the preparatory work required for the respective project. The
number and content of the required tasks will be announced at the beginning of the first semester.
The final grade for the module is calculated from the weighted partial grades of the individual examination components. In this context:
- the written assignments together account for 1/3,
- the project presentations together account for 1/3,
- the oral exam accounts for 1/3.
None.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107831 - Practice of Research (Project, 1st Semester) |
Assessment takes the form of an alternative type of assessment in accordance with §4 Abs. 2 Nr. 3 of the SPO.
Assignments to be completed during the semester:
◦ Project presentations,
◦ a discussion of the project’s content. The
number and content of the assignments will be announced at the beginning of the semester.
The final grade for the module is calculated from the weighted partial grades of the individual examination components. In this context:
- the written assignments together account for 1/3,
- the project presentations together account for 1/3,
- the oral exam accounts for 1/3.
None.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107831 - Practice of Research (Project, 1st Semester) |
Assessment takes the form of an alternative type of assessment in accordance with §4 Abs. 2 Nr. 3 of the SPO.
Assignments to be completed during the semester:
◦ written assignments,
◦ completion of the preparatory work required for the respective project. The
number and content of the required work will be announced at the beginning of the first semester.
The final grade for the module is calculated from the weighted partial grades of the individual examination components. In this context:
- the written assignments together account for 1/3,
- the project presentations together account for 1/3,
- the oral exam accounts for 1/3.
None.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107832 - Practice of Research (Project, 2nd Semester) |
Assessment is conducted in accordance with §4 Abs. 2 Nr. 2 of the SPO in the form of an oral exam (typically 30 minutes).
The final grade for the module is calculated from the weighted partial grades of the individual module components. In this context
, the written assignments together account for
1/3 of the total grade (T-INFO-T-INFO-115238),
the project presentations together have a weight of 1/3 (T-INFO-115237),
- and the oral exam has a weight of 1/3 (T-INFO-115236).
None.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107832 - Practice of Research (Project, 2nd Semester) |
Assessment takes the form of an alternative type of assessment in accordance with §4 Abs. 2 Nr. 3 of the SPO.
Assignments to be completed during the semester:
◦ Project presentations,
◦ a discussion of the project’s content. The number and content of the assignments will be announced at the beginning of the semester.
The final grade for the module is calculated from the weighted partial grades of the individual module components. In this context
, the written assignments together account for
1/3 of the total grade (T-INFO-T-INFO-115238),
the project presentations together have a weight of 1/3 (T-INFO-115237),
- and the oral exam has a weight of 1/3 (T-INFO-115236).
None.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105452 - Privacy Enhancing Technologies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400088 | Privacy Enhancing Technologies | 4 SWS | Lecture / Practice / 🗣 | Strufe, Guerra Balboa |
| Exams | |||||
| ST 2026 | 7500112 | Privacy Enhancing Technologies | Strufe | ||
| WT 26/27 | 7500308 | Privacy Enhancing Technologies | Strufe | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Dr. Maximilian Katzmann
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107168 - Probability and Computing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400153 | Probability and Computing | 3 SWS | Lecture / Practice / 🗣 | Sanders, Walzer, Hermann |
| Exams | |||||
| WT 26/27 | 7500376 | Probability and Computing | Bläsius, Sanders | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Basic knowledge of algorithms and data structures (e.g. from the lectures Algorithms 1 + 2) as well as basic knowledge of probability theory (e.g. from the lecture Introduction to Stochastics) are helpful.
| Coordinators: |
TT-Prof. Dr. Frederike Zufall
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107029 - Public International Law with an Economic Law Focus |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400172 | Public International Law with an Economic Law Focus | 2 SWS | Lecture / 🗣 | Kasper-Schöniger |
| Exams | |||||
| ST 2026 | 7500182 | Public International Law with an Economic Law Focus | Zufall | ||
| WT 26/27 | 7500066 | Public International Law | Zufall | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 (3) SPO) whether the performance assessment is carried out
- as an oral examination (duration approx. 20 mins.) (§ 4 Abs. 2 Nr. 2 SPO) or
- as a written examination (lasting 60 mins.) (§ 4 Abs. 2 No. 1 SPO).
None.
- General knowledge of (public) law (eg, through participating in public law or EU law modules) is helpful but not necessary.
- Interest in international affairs and politics is welcomed.
Competency Goals:
- Participating students will be able to navigate the plethora of multilateral treaties to detect relevant international law for specific cases.
- They can develop solutions for legal problems based on case law of international courts and tribunals.
- Students will be able to read and comprehend international treaties and case law.
- They will have a fundamental understand of the interplay between various subfields of public international law.
- Students can identify and explain current issues in public international law.
Content:
The lecture is designed to provide participating students with a general understanding of the foundations, subjects, and sources of public international law, its interplay with national legal regimes, and more detailed knowledge of particular subfields of public international law.
Since the lecture targets students of information systems, particular focus will be given to economic topics in international law, such as investment and trade law aspects. Due to the general importance of climate change for todays (economic) law, international climate change law and environmental law will form further focus areas.
In addition, a concise overview on human rights law, the law on State responsibility, and the peaceful settlement of disputes will be provided.
Throughout the lecture, important case law will be referenced and students are expected to read relevant cases in part to facilitate a discussion of such cases and their relevance for a subject field. Although the United Nations, including its principal judicial organ, the International Court of Justice, is one of the, if not the, key international organization in public international law, further international organizations (eg, Council of Europe, World Trade Organization) and their respective law(s) will also be touched.
Students are advised to have a statute book at hand that includes the most important international treaties and conventions (eg, Evans, Blackstone’s International Law Documents, currently 15th ed 2021).
Conducting the lecture in English intends to facilitate students to link their ideas and arguments to current debates in international law.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105623 - Reinforcement Learning |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400163 | Reinforcement Learning | Lecture / Practice / 🗣 | Neumann, Lioutikov | |
| Exams | |||||
| ST 2026 | 7500221 | Reinforcement Learning | Neumann, Lioutikov | ||
| WT 26/27 | 7500293 | Reinforcement Learning | Neumann, Lioutikov | ||
The success control takes place in the form of a written exam, usually 90 minutes in length, according to § 4 Abs. 2 Nr. 1 SPO.
A bonus can be acquired through successful participation in the exercise as a success control of a different kind (§4(2), 3 SPO 2008) or study performance (§4(3) SPO 2015). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. If the grade of the written examination is between 4.0 and 1.3, the bonus improves the grade by one grade level (0.3 or 0.4). The bonus is only valid for the main and post exams of the semester in which it was earned. After that, the grade bonus expires.
None.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100850 - Reliable Computing I |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424071 | Reliable Computing I | 2 SWS | Lecture / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500027 | Reliable Computing I | Tahoori | ||
| WT 26/27 | 7500167 | Reliable Computing I | Tahoori | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of Digital Design and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106654 - Research Focus Class: Blockchain & Cryptocurrencies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400184 | Research Focus Class: Blockchain & Cryptocurrencies Seminar | 2 SWS | Seminar / 🗣 | Hartenstein, Spiesberger, Grundmann |
| ST 2026 | 2400185 | Research Focus Class: Blockchain & Cryptocurrencies | 1 SWS | Lecture / 🗣 | Hartenstein, Spiesberger, Grundmann |
| Exams | |||||
| ST 2026 | 7500386 | Research Focus Class: Blockchain & Cryptocurrencies | Hartenstein | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). A presentation must be given.
None.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106654 - Research Focus Class: Blockchain & Cryptocurrencies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400184 | Research Focus Class: Blockchain & Cryptocurrencies Seminar | 2 SWS | Seminar / 🗣 | Hartenstein, Spiesberger, Grundmann |
| ST 2026 | 2400185 | Research Focus Class: Blockchain & Cryptocurrencies | 1 SWS | Lecture / 🗣 | Hartenstein, Spiesberger, Grundmann |
| Exams | |||||
| ST 2026 | 7500331 | Research Focus Class: Blockchain & Cryptocurrencies - Seminar | Hartenstein | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107728 - Research Practical Course on Behavior Foundation Models |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400139 | Research Practical Course on Behavior Foundation Models | 4 SWS | Practical course / 🗣 | Lioutikov |
| Exams | |||||
| ST 2026 | 7500266 | Research Practical Course on Behavior Foundation Models | Lioutikov | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Presentation on the chosen topic at the end of the semester and written elaboration.
None.
We highly recommend to take this research project in combination with the Seminar on Behavior Foundation Models.
It is highly recommended to attend the “Explainable Artificial Intelligence” lecture in parallel or prior to this project.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106866 - Research Practical Course: Artificial Intelligence & Security |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 24042 | Research Lab: Artificial Intelligence & Security | 4 SWS | Practical course / 🧩 | Wressnegger |
| Exams | |||||
| ST 2026 | 7500367 | Research Practical Course: Artificial Intelligence & Security | |||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106495 - Research Practical Course: Explainable Artificial Intelligence |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400142 | Forschungspraktikum: Explainable Artificial Intelligence | 4 SWS | Practical course / 🗣 | Lioutikov, Li, Mattes |
| Exams | |||||
| WT 26/27 | 7500360 | Research Practical Course: Explainable Artificial Intelligence | Lioutikov | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall impression is evaluated. The following partial aspects are included in the grading: Term paper (approx. 10 pages), Presentation (duration approx. 20 min.), Practical training (protocols)
Presentation on the chosen topic at the end of the semester and written paper.
None
We recommend taking this Praktikum after attending the “Explainable Artificial Intelligence” lecture in the summer semester.
We highly recommend to take this research project in combination with the “Explainable Artificial Intelligence” seminar.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
It is only possible to resign within two weeks after assignment of the topic.
| Coordinators: |
Prof. Dr. Bernhard Beckert
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107832 - Practice of Research (Project, 2nd Semester) |
Assessment takes the form of an alternative type of assessment in accordance with §4 Abs. 2 Nr. 3 of the SPO.
Requirements to be fulfilled during the semester:
◦ written assignments,
◦ completion of the preparatory work required for the respective project. The
number and content of the required work will be announced at the beginning of the semester.
The final grade for the module is calculated from the weighted partial grades of the individual module components. In this context
, the written assignments together account for
1/3 of the total grade (T-INFO-T-INFO-115238),
the project presentations together have a weight of 1/3 (T-INFO-115237),
- and the oral exam has a weight of 1/3 (T-INFO-115236).
None.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107174 - Research Project Deep Learning for Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400091 | Deep Learning for Robotics | 4 SWS | Practical course / 🗣 | Neumann |
| Exams | |||||
| WT 26/27 | 7500305 | Research Project Deep Learning for Robotics | Neumann | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
It is only possible to resign within two weeks after assignment of the topic.
Es müssen eine schriftliche Ausarbeitung erstellt und eine Präsentation gehalten werden.
- The discussed algorithms have to be implemented successfully.
- The experiments need to be conducted scientifically and need to be well documented.
- The final report is well written and well structured
- The final presentation is well prepared
None.
- Experience in Machine Learning is recommended.
- Python experience is recommended
- We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107163 - Research Project: Generative AI for Autonomous Agents |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400049 | Research Project: Generative AI for Autonomous Agents | 4 SWS | Practical course / 🗣 | Neumann |
| Exams | |||||
| ST 2026 | 7500326 | Research Project: Generative AI for Autonomous Agents | Neumann | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
- The discussed algorithms have to be implemented successfully.
- The experiments need to be conducted scientifically and need to be well documented.
- The final report is well written and well structured
- The final presentation is well prepared
None.
- Experience in Machine Learning is recommended.
- Python experience is recommended
- We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105591 - Resilient Networking |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400134 | Resilient Networking | 4 SWS | Lecture / Practice / 🧩 | Strufe |
| Exams | |||||
| ST 2026 | 7500249 | Resilient Networking | Strufe | ||
| WT 26/27 | 7500302 | Resilient Networking | Strufe | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Basics from cryptography and computer networks are helpful.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-107621 - Robotic Intelligence: Navigation and Mobile Manipulation |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2121333 | Robotic Intelligence: Navigation and Mobile Manipulation | 2 SWS | Lecture / 🗣 | Rönnau |
| ST 2026 | 2121334 | Robotic Intelligence: Navigation and Mobile Manipulation (Exercise) | 1 SWS | Practice / 🗣 | Rönnau |
| WT 26/27 | 2121333 | Robotic Intelligence: Navigation and Mobile Manipulation | 2 SWS | Lecture / 🗣 | Rönnau |
| WT 26/27 | 2121334 | Robotic Intelligence: Navigation and Mobile Manipulation (Exercise) | 1 SWS | Practice / 🗣 | Rönnau |
| Exams | |||||
| ST 2026 | 76-T-MACH-114952 | Robotic Intelligence: Navigation and Mobile Manipulation | Rönnau | ||
| ST 2026 | 76-T-MACH-114952-mdl | Robotic Intelligence: Navigation and Mobile Manipulation | Rönnau | ||
| WT 26/27 | 76-T-MACH-114952 | Robotic Intelligence: Navigation and Mobile Manipulation | Rönnau | ||
Written exam: duration 60 minutes. If the written exam is passed, a grade bonus can be achieved by successfully completing the exercises.
The partial performance "T-MACH-114034 - Robotic Intelligence for Mobile Systems" must not have been started.
The course is offered in English.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-107558 - Robotic Intelligence: Perception and Scene Understanding |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2121331 | Robotic Intelligence: Perception and Scene Understanding | 2 SWS | Lecture / 🗣 | Rönnau |
| WT 26/27 | 2121332 | Robotic Intelligence: Perception and Scene Understanding (Exercise) | 1 SWS | Practice / 🗣 | Rönnau |
| Exams | |||||
| ST 2026 | 76-T-MACH-114813 | Robotic Intelligence: Perception and Scene Understanding | Rönnau | ||
| WT 26/27 | 76-T-MACH-114813 | Robotic Intelligence: Perception and Scene Understanding | Rönnau | ||
Written exam: duration 60 minutes. A grade bonus can be achieved by successfully completing the exercises.
"T-MACH-114034 - Robotic Intelligence for Mobile Systems" must not have started.
The course is offered in English.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107162 - Robotics I - Introduction to Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424152 | Robotics I - Introduction to Robotics | 4 SWS | Lecture / 🗣 | Asfour, Mombaur |
| Exams | |||||
| ST 2026 | 7500218 | Robotics I - Introduction to Robotics | Asfour | ||
| WT 26/27 | 7500106 | Robotics I - Introduction to Robotics | Asfour | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
none.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107123 - Robotics II - Humanoid Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400074 | Robotics II: Humanoid Robotics | 2 SWS | Lecture / 🗣 | Asfour |
| Exams | |||||
| ST 2026 | 7500086 | Robotics II: Humanoid Robotics | Asfour | ||
| WT 26/27 | 7500211 | Robotics II: Humanoid Robotics | Asfour | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
Having visited the lecture on Robotics I – Introduction to Robotics is recommended
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107130 - Robotics III - Sensors and Perception in Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400067 | Robotics III: Sensors and Perception in Robotics | 2 SWS | Lecture / 🗣 | Asfour, Triebel |
| Exams | |||||
| ST 2026 | 7500242 | Robotics III - Sensors and Perception in Robotics | Asfour | ||
| WT 26/27 | 7500207 | Robotics III - Sensors and Perception in Robotics | Asfour | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.
none.
Having visited the lectures on Robotics I – Introduction to Robotics is recommended.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
TT-Prof. Dr. Barbara Bruno
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107598 - Robotics Institute Germany - Lecture Series |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400246 | RIG Lecture Series | 2 SWS | Lecture / 🧩 | Bruno, Niehues, Asfour |
| Exams | |||||
| ST 2026 | 7500420 | Robotics Institute Germany - Lecture Series | Bruno, Asfour | ||
| WT 26/27 | 7500401 | Robotics Institute Germany - Lecture Series | Bruno, Asfour | ||
Depending on the number of attending students, the assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting approx. 20 minutes or as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting approx. 120 minutes. The art of assessment will be announced at least six weeks before the end of the course. The default type of examination is oral.
None.
Knowledge of the content of module Robotics I - Introduction to Robotics is helpful.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107090 - Sampling Methods for Machine Learning |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400194 | Sampling Methods for Machine Learning | 3 SWS | Lecture / Practice / 🧩 | Hanebeck, Frisch |
| Exams | |||||
| ST 2026 | 7500391 | Sampling Methods for Machine Learning | Hanebeck | ||
| WT 26/27 | 7500533 | Sampling Methods for Machine Learning | Hanebeck | ||
The assessment is carried out as an oral examination, lasting 20 minutes in accordance with Section 4 (2) No. 2 SPO.
Additional certificate for digital exercise (Übungsschein)
Knowledge of a higher programming language with sophisticated libraries for scientific-numerical computing (e.g. Julia, Matlab, Python) is advantageous.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107090 - Sampling Methods for Machine Learning |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400194 | Sampling Methods for Machine Learning | 3 SWS | Lecture / Practice / 🧩 | Hanebeck, Frisch |
| Exams | |||||
| ST 2026 | 7500392 | Sampling Methods for Machine Learning - Pass | Hanebeck | ||
| WT 26/27 | 7500534 | Sampling Methods for Machine Learning - Pass | Hanebeck | ||
Digital exercise:
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Knowledge of a higher programming language with sophisticated libraries for scientific-numerical computing (e.g. Julia, Matlab, Python) is advantageous
| Coordinators: |
Prof. Dr. Henning Meyerhenke
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107867 - Scalable Graph and Sparse Matrix Algorithms for Learning |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400188 | Scalable Graph and Sparse Matrix Algorithms for Learning | 3 SWS | Lecture / 🗣 | Meyerhenke |
The assessment takes the form of an oral examination lasting approx. 30 minutes in accordance with § 4 Abs. 2 No. 2 of the Examination Regulations (SPO). A programming exercise must be completed as a prerequisite for the exam.
None.
Basic knowledge of graph algorithms, linear algebra, and C++.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105780 - Scientific Methods to Design and Analyze Secure Decentralized Systems |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400009 | Scientific Methods to Design and Analyze Secure Decentralized Systems | 4 SWS | Lecture / Practice / 🗣 | Hartenstein, Jacob |
| Exams | |||||
| ST 2026 | 7500081 | Scientific Methods to Design and Analyze Secure Decentralized Systems | Hartenstein | ||
| WT 26/27 | 7500050 | Scientific Methods to Design and Analyze Secure Decentralized Systems | Hartenstein | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take the form of an oral examination of approx.
- in the form of an oral examination of approx. 30 minutes in accordance with § 4 Para. 2 No. 2 SPO
or
- in the form of a written examination in accordance with § 4 Para. 2 No. 1 SPO
takes place.
None.
Prior knowledge on the abstract concepts as well as concrete use cases of decentralized systems is strongly recommended. The “Decentralized Systems: Fundamentals, Modeling, and Applications” lecture covers all necessary aspects, but equivalent lectures and / or self-study can also be sufficient.
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Lena Coerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107254 - Interdisciplinary Qualifications |
This module component can be used for self service assignment of grades acquired from the following study providers:
Interdisciplinary qualifications (IQ) completed at the House-of-Competence (HoC),
at the Zentrum für Angewandte Kulturwissenschaften (ZAK) or at the Sprachenzentrum (SpZ)
can be assigned in self-service.
First, select a partial accomplishment named "self-assignment" in your study schedule
and second, assign an IQ-achievement via the tab "IQ achievements".
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-102662 - Seminar Dependable Computing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400030 | Dependable Computing | 2 SWS | Seminar / 🧩 | Tahoori |
| WT 26/27 | 2400030 | Dependable Computing | 2 SWS | Seminar / 🗣 | Tahoori |
| Exams | |||||
| ST 2026 | 7500118 | Seminar Dependable Computing | Tahoori | ||
| WT 26/27 | 7500152 | Seminar Dependable Computing | Tahoori | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
The module grade is made up of 50% of the presentation and 50% of the written paper.
None.
Knowledge of "Dependable Computing" and "Fault Tolerant Computing" and computer architecture is helpful.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107846 - Seminar for Practical Course: Machine Learning and Intelligent Systems |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400067 | Seminar for Practical Course: Machine Learning and Intelligent Systems | 2 SWS | Seminar / 🗣 | Hanebeck |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall assessment consists of a written paper-style report, a presentation, and an assessment of the scientific work carried out during the seminar.
None.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107242 - Seminar in Privacy |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400087 | Seminar in Privacy | 2 SWS | Seminar / 🗣 | Strufe, Guerra Balboa |
| Exams | |||||
| ST 2026 | 7500323 | Seminar in Privacy | Strufe | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given; in addition, preliminary papers must be submitted and commented on in a peer review between fellow students. Withdrawal is possible within two weeks of the topic being assigned.
None.
Fundamentals of IT security, computer networks and distributed systems are required
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107729 - Seminar on Behavior Foundation Models |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400136 | Seminar on Behavior Foundation Models | 2 SWS | Seminar / 🗣 | Lioutikov |
| Exams | |||||
| ST 2026 | 7500270 | Seminar on Behavior Foundation Models | Lioutikov | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Presentation on the chosen topic at the end of the semester and written elaboration.
None.
We highly recommend to take this seminar in combination with the Research Practical Course on Behavior Foundation Models (Forschungspraktikum).
It is highly recommended to attend the “Explainable Artificial Intelligence” lecture in parallel or prior to this seminar.
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We might use the PyTorch deep learning library In the exercises. Some prior knowledge in this is helpful but not necessary.
| Coordinators: |
PD Dr. Bastian Breustedt
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-107839 - Seminar Radiation Protection in Medicine |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2305290 | Seminar Radiation Protection in Medicine | 2 SWS | Seminar / 🗣 | Breustedt |
| Exams | |||||
| ST 2026 | 7305294 | Dosimetry of Ionising Radiation | Breustedt | ||
The examination consists of a written handout (approx. 2-3 pages) and an oral presentation (approx. 25-35 min.) of the students work. Considered will be the quality of the contents, the style of the presentation, the quality and content of the handout. The overall impression is rated.
none
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107353 - Seminar: Advanced Topics in Natural Language Processing |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400217 | Advanced Topics in Natural Language Processing | 2 SWS | Seminar | Niehues |
| WT 26/27 | 2400074 | Advanced Topics in Natural Language Processing | 2 SWS | Seminar | Niehues, Li |
| Exams | |||||
| ST 2026 | 00037 | Advanced Topics in Natural Language Processing | Niehues | ||
| WT 26/27 | 7500267 | Seminar Advanced Topics in Natural Language Processing | Niehues | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge from the lectures Machine Translation and Cognitive Systems
| Coordinators: |
Dr. Ashlin Iser
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107209 - Seminar: Advanced Topics on SAT Solving |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400020 | Advanced Topics in SAT Solving | 2 SWS | Seminar / 🗣 | Sanders, Iser, Schreiber |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A presentation must be given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of the basics from "SAT Solving in Practice" is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106086 - Seminar: Algorithm Engineering |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of algorithms is an advantage. Exemplary lectures are Algorithms I, Algorithms II, Algorithm Engineering and Parallel Algorithms.
| Coordinators: |
TT-Prof. Dr. Benjamin Schäfer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106490 - Seminar: Artificial Intelligence for Energy Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400259 | Seminar: Artificial Intelligence for Energy Systems | Seminar / 🗣 | Schäfer | |
| WT 26/27 | 2400175 | Seminar: Artificial Intelligence for Energy Systems | 2 SWS | Seminar / 🗣 | Schäfer |
| Exams | |||||
| ST 2026 | 7500425 | Seminar: Artificial Intelligence for Energy Systems | Schäfer | ||
| WT 26/27 | 7500354 | Seminar: Artificial Intelligence for Energy Systems | Schäfer | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), consisting of a Term paper (max. 15 pages) and a Presentation (duration approx. 30 min.)
The grading scale will be announced in the course.
Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
None.
Previous participation in “Energieinformatik 1” and/or “Energieinformatik 2” is beneficiary but not mandatory.
| Coordinators: |
Prof. Dr.-Ing. Arne Rönnau
|
|---|---|
| Organisation: |
KIT Department of Mechanical Engineering |
| Part of: |
M-MACH-106902 - Seminar: Bionic Algorithms and Robot Technologies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2121343 | Seminar: Bionic Algorithms and Robot Technologies | 2 SWS | Seminar / 🗣 | Rönnau |
| WT 26/27 | 2121343 | Seminar: Bionic Algorithms and Robot Technologies | 2 SWS | Seminar / 🗣 | Rönnau |
| Exams | |||||
| ST 2026 | 76-T-MACH-113842 | Seminar: Bionic Algorithms and Robot Technologies | Rönnau | ||
| WT 26/27 | 76-T-MACH-113842 | Seminar: Bionic Algorithms and Robot Technologies | Rönnau | ||
Success is assessed through the preparation of a written seminar paper and its presentation as an examination of a different kind.
None
Attending the lecture "Biologically Inspired Robots" is helpful.
The course is offered in English.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105309 - Seminar: Continuous Software Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400166 | Seminar Continuous Software Engineering | 2 SWS | Seminar | Koziolek |
| WT 26/27 | 2400108 | Continuous Software Engineering | 2 SWS | Seminar | Koziolek |
| Exams | |||||
| ST 2026 | 7500395 | Seminar: Continuous Software Engineering | Koziolek | ||
| WT 26/27 | 7500243 | Seminar: Continuous Software Engineering | Koziolek | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
- the preparation of a written paper (50%)
- the assessment of two seminar papers as part of a peer review (10%)
- the preparation of presentation slides and giving a presentation (20%)
- punctuality of submissions (20%)
None.
| Coordinators: |
Prof. Dr. Pascal Friederich
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106958 - Seminar: Critical Topics in AI |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400215 | Seminar: Critical topics in AI | 2 SWS | Seminar / 🗣 | Friederich, Torresi, Neubert, Schlöder, Metni |
| WT 26/27 | 2400231 | Seminar: Critical topics in AI | 2 SWS | Seminar / 🧩 | Friederich, Torresi, Neubert, Schlöder, Metni |
| Exams | |||||
| ST 2026 | 7500408 | Seminar: Critical topics in AI | Friederich | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The following partial aspects are included in the grading: Term paper (approx. 10-15 pages), presentation (duration 30+15 min.). The grading scale will be announced in the course. Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
Basic knowledge in AI and Machine Learning, e.g.
• BA Informatics: Introduction to artificial intelligence
Interest in social topics and research questions is required
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107027 - Seminar: Current Trends in Theoretical Computer Science |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400101 | Current Trends in Theoretical Computer Science | Seminar / 🗣 | Künnemann-Goos | |
| Exams | |||||
| ST 2026 | 7500411 | Seminar: Current Trends in Theoretical Computer Science | Künnemann-Goos | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO) and consists of the overall impression during the seminar, including the presentation as session leader and a scientific report at the end of the seminar.
None.
Basic knowledge of theoretical computer science and algorithm design is recommended.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107175 - Seminar: Deep Learning for Robotics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400099 | Deep Learning for Robotics | 2 SWS | Seminar / 🗣 | Neumann |
| Exams | |||||
| WT 26/27 | 7500306 | Seminar: Deep Learning for Robotics | Neumann | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Presentation on the chosen topic at the end of the semester and written elaboration
Withdrawal is possible within two weeks of the topic being assigned.
None.
Attendance of the lecture "Machine Learning - Fundamentals and Algorithms" is recommended.
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107231 - Seminar: Embedded Systems I |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of IoT and embedded systems
| Coordinators: |
Prof. Dr.-Ing. Jörg Henkel
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107232 - Seminar: Embedded Systems II |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400129 | Internet of Things | Seminar / 🧩 | Henkel | |
| ST 2026 | 2400137 | Embedded Machine Learning | Seminar / 🧩 | Henkel, Sikal, Khdr, Ahmed, Demirdag, Mentzos | |
| ST 2026 | 2400148 | Embedded Security and Architectures | Seminar / 🧩 | Henkel, Nassar, Khdr, Sikal, Tobar, Alsharkawy, Dietrich | |
| Exams | |||||
| ST 2026 | 7500332 | CES - Seminar: Internet of Things | Henkel | ||
| ST 2026 | 7500335 | CES - Seminar: Machine Learning | Henkel | ||
| ST 2026 | 7500336 | CES - Seminar: Embedded Systems: Architectures and Technologies, Embedded Security and Architectures | Henkel | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of IoT and embedded systems
This is identical to the module 'Seminars: Embedded Systems I' and enables participation in a second seminar at the CES Chair.
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106927 - Seminar: Exoskeletons & Motion Capture |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400187 | Seminar: Exoskeletons & Motion Capture | 2 SWS | Seminar / 🗣 | Mombaur, Lau |
| Exams | |||||
| WT 26/27 | 7500161 | Seminar: Exoskeletons & Motion Capture | Mombaur | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
This includes the preparation of a term paper in form of a scientific paper (6 pages double column) an oral presentation with slides (30 Min + 15 Min discussion) . Students may redraw from the examination during the first two weeks after the topic has been communicated.
Participation in the block seminar is mandatory.
Students have to actively participate in all discussions.
None.
Knowledge in Robotics (e.g. from the class Robotics 1 and follow-ups) is helpful
| Coordinators: |
TT-Prof. Dr. Rudolf Lioutikov
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106497 - Seminar: Explainable Artificial Intelligence |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400143 | Seminar: Explainable Artificial Intelligence | 2 SWS | Seminar / 🗣 | Lioutikov, Mattes, Li |
| Exams | |||||
| WT 26/27 | 7500384 | Seminar: Explainable Artificial Intelligence | Lioutikov | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Presentation on the chosen topic at the end of the semester and written paper.
None.
We recommend taking this research project after attending the “Explainable Artificial Intelligence” lecture in the summer semester.
We highly recommend to take this seminar in combination with the “Explainable Artificial Intelligence” research project (Forschungspraktikum).
• Experience in Machine Learning is recommended, e.g. through prior coursework.
◦ The Computer Science Department offers several great lectures e.g., “Maschinelles Lernen - Grundlagen und Algorithmen” and “Deep Learning ”
• A good mathematical background will be beneficial
• Python experience is recommended
• We will use the PyTorch deep learning library. Some prior knowledge in this is helpful but not necessary.
It is only possible to resign within two weeks after assignment of the topic.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106651 - Seminar: Exploring Robotics - Insights from Science Fiction, Research and Society |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400161 | Exploring Robotics: Insights from Science Fiction, Research and Society | 2 SWS | Seminar / 🗣 | Bruno, Maure, Yang |
| Exams | |||||
| ST 2026 | 7500110 | Seminar: Exploring Robotics - Insights from Science Fiction, Research and Society | Bruno | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
The overall impression is evaluated. The following partial aspects are included in the grading: Term paper (approx. 6 pages in double-column format), Presentation (duration approx. 10+10 min.).
None.
Knowledge of the content of modules Robotics I - Introduction to Robotics, Robotics II: Humanoid Robotics, Robotics III - Sensors and Perception in Robotics is helpful.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107436 - Seminar: Failing Forward: Robotics, Critical Reading and the Making of Good Research |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400194 | Failing Forward: Robotics, Critical Reading and the Making of Good Research - Seminar | 2 SWS | Seminar / 🗣 | Bruno, Maure, Neef |
| Exams | |||||
| WT 26/27 | 7500389 | Seminar: Failing Forward: Robotics, Critical Readig and the Making of Good Research | Bruno | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). The overall impression is evaluated. The following partial aspects are included in the grading: Term paper (approx. 6 pages in double-column format), Presentation (duration approx. 10+10 min.).
None.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106645 - Seminar: Fine-Grained Complexity Theory & Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400153 | Fine-Grained Complexity Theory & Algorithms | 2 SWS | Seminar / 🗣 | Künnemann-Goos |
| Exams | |||||
| ST 2026 | 7500352 | Seminar: Fine-Grained Complexity Theory & Algorithms | Künnemann-Goos | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO) and consists of a presentation and a scientific report.
None.
Basic knowledge of theoretical computer science and algorithm design is recommended.
Concurrent or previous attendance of the lecture “Fine-Grained Complexity Theory & Algorithms” is helpful, but not required. This seminar can be attended independently.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106868 - Seminar: Hot Topics in Artificial Intelligence & Security 1 |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 24004 | Seminar: Hot Topics in Security of Machine Learning | 2 SWS | Seminar / 🗣 | Wressnegger, Zhao |
| WT 26/27 | 24005 | Seminar: Hot Topics in Cyber-Physical Systems Security | 2 SWS | Seminar / 🗣 | Wressnegger |
| Exams | |||||
| WT 26/27 | 7500359 | Seminar: Hot Topics in Security of Machine Learning | Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. One repetition is possible.
None.
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106869 - Seminar: Hot Topics in Artificial Intelligence & Security 2 |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24003 | Seminar: Hot Topics in Machine Learning for Computer Security | 2 SWS | Seminar / 🗣 | Wressnegger |
| WT 26/27 | 24004 | Seminar: Hot Topics in Security of Machine Learning | 2 SWS | Seminar / 🗣 | Wressnegger, Zhao |
| WT 26/27 | 24005 | Seminar: Hot Topics in Cyber-Physical Systems Security | 2 SWS | Seminar / 🗣 | Wressnegger |
| Exams | |||||
| ST 2026 | 7500198 | Seminar: Hot Topics in Machine Learning for Computer Security | Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written elaboration must be prepared and a presentation must be given. Withdrawal is possible within two weeks after assignment of the topic. One repetition is possible.
None.
The basics of IT security and artificial intelligence are a prerequisite.
| Coordinators: |
Prof. Dr. Alexandros Stamatakis
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100750 - Seminar: Hot Topics in Bioinformatics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400011 | Hot Topics in Bioinformatics | 2 SWS | Seminar / 🗣 | Stamatakis |
| Exams | |||||
| ST 2026 | 7500014 | Seminar: Hot Topics in Bioinformatics | Stamatakis | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO). (Weighting of presentation and written report: 50% each)
The exam in Introduction to Bioinformatics for Computer Scientists must have been passed in one of the preceding semesters.
Basic knowledge in the areas of theoretical computer science (algorithms, data structures) and technical computer science (sequential optimisation in C or C++, computer architectures, parallel programming, vector processors) will be beneficial.
| Coordinators: |
Prof. Dr. Hannes Hartenstein
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-104891 - Seminar: Hot Topics in Decentralized Systems |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500297 | Seminar: Hot Topics in Decentralized Systems | Hartenstein | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of the basics of IT security management for networked systems and the basic security module is helpful.
| Coordinators: |
Prof. Dr. Christian Wressnegger
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106392 - Seminar: Hot Topics in Explainable Artificial Intelligence (XAI) |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24005 | Seminar: Hot Topics in Explainable Artificial Intelligence (XAI) | 2 SWS | Seminar / 🗣 | Wressnegger, Noppel |
| Exams | |||||
| ST 2026 | 7500225 | Seminar: Hot Topics in Explainable Artificial Intelligence (XAI) | Wressnegger | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper (seminar paper) must be prepared and a presentation must be given.
None.
| Coordinators: |
Prof. Dr.-Ing. Klemens Böhm
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107686 - Seminar: Information Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400131 | Advances in Data Science | 2 SWS | Seminar / 🗣 | Böhm, Matteucci, Friederich |
| Exams | |||||
| ST 2026 | 7500100 | Seminar Information Systems | Böhm | ||
| WT 26/27 | 7500055 | Seminar Information Systems | Reimann | ||
Success is assessed by preparing a written seminar paper and presenting it as a graded assessment of a different kind in accordance with Section 4 (2) No. 3 SPO. The seminar grade corresponds to the written performance, but can be lowered or raised by up to two grade levels by the presentation performance. If the seminar paper is discontinued after the topic has been issued, the seminar is assessed with a grade of 5.0.
The coursework is passed if the written paper and the presentations have each been passed individually; regular attendance at the sessions and active participation in the content-related discussions are also required to pass.
None.
Lectures of the Chair of Information Management Systems that match the topic of the seminar are strongly recommended.
| Coordinators: |
Jun.-Prof. Dr. Jan Stühmer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107217 - Seminar: Interpretability and Causality in Machine Learning |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400181 | Interpretability and Causality in Machine Learning | 2 SWS | Seminar / 🗣 | Stühmer |
| Exams | |||||
| ST 2026 | 7500319 | Seminar: Interpretability and Causality in Machine Learning | Stühmer | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written elaboration must be prepared and a presentation must be given. Students may redraw from the examination during the first two weeks after the topic has been communicated. The assessment can be repeated once.
None.
Attendance of the lecture "Machine Learning - Fundamentals and Algorithms" is recommended.
| Coordinators: |
Gustavo Gil Gasiola
Maitrayee Pathak
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107028 - Seminar: Law and Legal Studies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400171 | Regulating AI: from ethics to law | 2 SWS | Seminar / 🗣 | Gil Gasiola |
| ST 2026 | 2400177 | Designing Data Governance of Digital Systems | 2 SWS | Seminar / 🗣 | Pathak |
| ST 2026 | 2400190 | EU Digital Regulatory Framework | 2 SWS | Seminar / 🗣 | Zufall |
| WT 26/27 | 2400177 | Human vs. Machine: (Un-)Biased Legal Decision-Making? | 2 SWS | Seminar / 🗣 | Holzhausen |
| WT 26/27 | 2400184 | EU Digital Regulatory Framework | 2 SWS | Seminar / 🗣 | Zufall |
| WT 26/27 | 2400203 | EU Fundamental Rights and EU Economic Law in Smart Cities | 2 SWS | Seminar / 🗣 | Kasper-Schöniger |
| WT 26/27 | 2400221 | Digital Platforms on Trial: EU and US Case Law Perspectives | 2 SWS | Seminar / 🗣 | Abhilasha |
| Exams | |||||
| ST 2026 | 7500060 | Seminar: Law and Legal Studies | Zufall | ||
| WT 26/27 | 7500311 | Seminar: Legal Studies I | Zufall | ||
| WT 26/27 | 7500363 | Seminar: Law and Legal Studies | Zufall | ||
Success is assessed by preparing a written seminar paper and its presentation as a different type of examination in accordance with Section 4 (2) No. 3 SPO.
None.
| Coordinators: |
TT-Prof. Dr. Peer Nowack
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106719 - Seminar: Machine Learning in Climate and Environmental Sciences |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400207 | Seminar Machine Learning in Climate and Environmental Sciences | 2 SWS | Seminar / 🗣 | Nowack, Amiramjadi |
| Exams | |||||
| ST 2026 | 7500213 | Seminar Machine Learning in Climate and Environmental Sciences | Nowack | ||
| WT 26/27 | 7500071 | Seminar Machine Learning in Climate and Environmental Sciences | Nowack | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
In the form of a written seminar paper and the presentation of the same.
• Familiarity with machine learning concepts and techniques.
• Basic knowledge of climate and environmental scince is advantageous but not mandatory.
• An interest in climate and environmental sciences topics is a prerequisite.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107816 - Seminar: Neural Networks and Artificial Intelligence |
Progress is assessed through an examination of another type in accordance with § 4(2)(3) of the Study Regulations.
None.
Advanced Artificial Intelligence
| Coordinators: |
Prof. Dr.-Ing. Frank Bellosa
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107205 - Seminar: Operating Systems |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24346 | Seminar Hot Topics in Modern Operating Systems | 2 SWS | Seminar / 🗣 | Bellosa, Khalil |
| WT 26/27 | 2400017 | Hot Topics in Modern Operating Systems | 2 SWS | Seminar / 🗣 | Bellosa, Khalil |
| Exams | |||||
| ST 2026 | 7500165 | Seminar: Operating Systems | Bellosa | ||
| WT 26/27 | 7500223 | Seminar: Operating Systems | Bellosa | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), by preparing a written seminar paper and the presentation of the same.
The overall grade is made up of the graded and weighted performance assessments (usually 50 % seminar paper, 50 % presentation). An overall grade is awarded.
None.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105585 - Seminar: Post-Quantum Cryptography |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400002 | Post-Quantum Cryptography | 2 SWS | Seminar / 🗣 | Klooß, Müller-Quade |
| WT 26/27 | 2400126 | Seminar Post-Quantum Cryptography | 2 SWS | Seminar / 🗣 | Müller-Quade, Kühn, Ottenhues, Klooß |
| Exams | |||||
| ST 2026 | 7500015 | Seminar: Post-Quantum Cryptography | Müller-Quade | ||
| WT 26/27 | 7500327 | Seminar: Post-Quantum Cryptography | Müller-Quade | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned
None.
Basic knowledge of IT-Security and cryptography are recommended.
| Coordinators: |
Prof. Dr. Henning Meyerhenke
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107264 - Seminar: Practical Graph Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400196 | Practical Graph Algorithms | 2 SWS | Seminar | Meyerhenke |
| Exams | |||||
| ST 2026 | 754600002 | Seminar: Practical Graph Algorithms | Meyerhenke | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of algorithms, in particular graph algorithms, is a clear advantage. Exemplary lectures are Algorithms I and Algorithms II.
| Coordinators: |
Prof. Dr. Thorsten Strufe
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107216 - Seminar: Privacy and Security |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400118 | Seminar Privacy and Security | 2 SWS | Seminar | Strufe, Guerra Balboa |
| Exams | |||||
| WT 26/27 | 7500127 | Seminar Privacy and Security | Strufe | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and a presentation given; in addition, preliminary papers must be submitted and commented on in a peer review between fellow students. Withdrawal is possible within two weeks of the topic being assigned.
None.
Fundamentals of IT security, computer networks and distributed systems are required
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-103306 - Seminar: Proofs from THE BOOK |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400033 | Seminar: Proofs from THE BOOK | 2 SWS | Seminar / 🗣 | Sanders, Walzer, Lehmann |
| WT 26/27 | 2400171 | Seminar: Proofs from THE BOOK | 2 SWS | Seminar / 🗣 | Sanders, Walzer, Hermann |
| Exams | |||||
| ST 2026 | 7500301 | Seminar: Proofs from THE BOOK | Sanders | ||
| WT 26/27 | 7500059 | Seminar: Proofs from THE BOOK | Sanders | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
The student must present multiple proofs over the course of the semester and moderate the ensuing discussion about those proofs. No written documents are required. Students may redraw from their participation until the end of the second seminar date.
None.
The German version “Das Buch der Beweise” is available online at the KIT library within the KIT network. The English version “Proofs from THE BOOK” is available as a physical copy at the KIT library. We recommend having a look inside either version before registering for this seminar.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105408 - Seminar: Quantum Information Theory |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400085 | Quantum Information Theory | 2 SWS | Seminar / 🗣 | Müller-Quade, Fruböse, Meier, Jiang |
| Exams | |||||
| ST 2026 | 7500267 | Seminar: Quantum Information Theory | Müller-Quade | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A presentation must be given and a written elaboration of exercises must be prepared.
Withdrawal is possible within two weeks of the topic being assigned.
None.
Basic knowledge of IT-Security and linear algebra are recommended.
| Coordinators: |
TT-Prof. Dr. Thomas Bläsius
Prof. Dr. Peter Sanders
Dr. rer. nat. Torsten Ueckerdt
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107172 - Seminar: Recent Highlights in Algorithms |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO), by preparing a written seminar paper and the presentation of the same
None.
Knowledge of the basics of graph theory and algorithm technology is helpful.
| Coordinators: |
Prof. Dr. Gerhard Neumann
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107926 - Seminar: Robot Reinforcement Learning |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400084 | Seminar: Robot Reinforcement Learning | 2 SWS | Seminar / 🗣 | Neumann |
| ST 2026 | 2400264 | Seminar: Robot Reinforcement Learning | 2 SWS | Seminar / 🗣 | Neumann |
| Exams | |||||
| ST 2026 | 7500277 | Seminar: Robot Reinforcement Learning | Neumann | ||
| ST 2026 | 7500432 | Seminar: Robot Reinforcement Learning | Neumann | ||
Assessment is based on a graded written seminar paper and the presentation of that paper in the form of an examination of another type pursuant to § 4 (2) No. 3 of the SPO.
None.
| Coordinators: |
Prof. Dr. Jörn Müller-Quade
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-105761 - Seminar: Secure Multiparty Computation |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400088 | Seminar Secure Multiparty Computation | 2 SWS | Seminar / 🗣 | Jiang, Müller-Quade |
| Exams | |||||
| WT 26/27 | 7500326 | Seminar: Secure Multiparty Computation | Müller-Quade | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
A written paper must be prepared and/or a presentation must be given. Withdrawal is possible within two weeks of the topic being assigned.
None.
Knowledge of the content of the lecture Cryptographic Protocols is assumed.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107236 - Seminar: Software Architecture, Security and Privacy |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400060 | Seminar: Software Architecture, Security and Privacy | 2 SWS | Seminar / 🗣 | Reussner, Raabe, Werner, Müller-Quade |
| Exams | |||||
| WT 26/27 | 7500232 | Seminar Data in Software-Intensive Technical Systems – Modeling – Analysis – Protection | Reussner | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
| Coordinators: |
Prof. Dr. Jan Niehues
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107179 - Seminar: Speech-to-Speech Translation |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400032 | Seminar Speech-to-Speech Translation | 2 SWS | Seminar / 🗣 | Niehues |
| Exams | |||||
| ST 2026 | 7500046 | Seminar Speech-to-Speech Translation | Waibel, Niehues | ||
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
None.
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107625 - Seminar: Type Systems and Static Program Analysis |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400267 | Type Systems and Static Program Analysis | 2 SWS | Seminar / 🧩 | Erdweg, Lego, Marx |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO).
Students must write a seminar paper, deliver a presentation, and participate in the discussion. Withdrawal is possible within two weeks after the topic has been assigned.
None.
Students should be familiar with the foundational knowledge about type systems (e.g., typing rules, type checking, polymorphism) or static program analysis (e.g., data flow, abstract interpretation).
| Coordinators: |
Prof. Dr. Gerhard Satzger
Prof. Dr. Orestis Terzidis
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-WIWI-101503 - Service Design Thinking |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2595600 | Service Design Thinking | 2 SWS | Lecture / 🗣 | Osaro, Terzidis, Feldmann |
| WT 26/27 | 2595600 | Service Design Thinking | 2 SWS | Lecture / 🗣 | Terzidis, Satzger, Osaro, Feldmann |
Success is assessed in the form of an alternative exam assessment which consists of a case study, workshops, and a final presentation. The weighting of these components for the grade will be announced at the beginning of the course.
None
This course is held in English – proficiency in writing and communication is required.
Our past students recommend to take this course at the beginning of the masters program.
Due to practical project work as a component of the program, access is limited. The module (as well as the module component) spans two semesters. It starts in September every year and runs until end of June in the subsequent year. Entering the program is only possible at its beginning - after prior application in May/June. For more information on the application process and the program itself are provided in the module component description and the program's website (https://sdtkarlsruhe.de/). Furthermore, the lecturers provide an information event for applicants every year in May.
| Coordinators: |
Prof. Dr. Orestis Terzidis
|
|---|---|
| Organisation: |
KIT Department of Business and Economics |
| Part of: |
M-ETIT-105073 - Student Innovation Lab |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2545082 | SIL Entrepreneurship Project | Seminar / 🖥 | Mitarbeiter | |
| WT 26/27 | 2545082 | SIL Entrepreneurship Project | 4 SWS | Seminar | Terzidis |
| Exams | |||||
| WT 26/27 | 7900037 | SIL Entrepreneurship Project | Terzidis | ||
Success is assessed in the form of an alternative exam assessment. The grade results from the evaluation of two seminar papers (each approx. 20 pages). Further details will be provided at the beginning of the course. The points system for the assessment of the two seminar papers is determined by the lecturer of the course. It will be announced at the beginning of the course.
None
None
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106504 - Simulation and Optimization in Robotics and Biomechanics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400160 | Simulation and Optimization in Robotics and Biomechanics | 4 SWS | Lecture / Practice / 🗣 | Mombaur, Ackermann |
| Exams | |||||
| ST 2026 | 7500419 | Simulation and Optimization in Robotics and Biomechanics | Mombaur | ||
| WT 26/27 | 7500355 | Simulation and Optimization in Robotics and Biomechanics | Mombaur | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 No. 1 SPO) lasting up to 30 minutes. It can be repeated once.
As a prerequisite for the participation in the oral exam, students must regularly and successfully participate in the exercises. Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course.
T-INFO-114838 – Simulation and Optimization in Robotics and Biomechanics – Pass grade is required. Completion of module Robotics 1 or corresponding knowledge required
Programing skills in C/C++
Limitation to 30 participants
| Coordinators: |
Prof. Dr. Katja Mombaur
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106504 - Simulation and Optimization in Robotics and Biomechanics |
| Prerequisite for: |
T-INFO-113123 - Simulation and Optimization in Robotics and Biomechanics |
| Exams | |||||
|---|---|---|---|---|---|
| WT 26/27 | 7500313 | Simulation and Optimization in Robotics and Biomechanics - Pass | Mombaur | ||
| WT 26/27 | 7500355 | Simulation and Optimization in Robotics and Biomechanics | Mombaur | ||
Success is assessed in the form of coursework in accordance with Section 4 (3) SPO (ungraded exercise certificate).
Exercise sheets must be handed in regularly to pass the course. The specific details will be announced in the lecture.
Participation in the exercise is compulsory and the contents of the exercise are relevant for the examination.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107237 - Software Architecture and Quality |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424667 | Software Architecture and Quality | 2 SWS | Lecture / 🗣 | Reussner |
| Exams | |||||
| ST 2026 | 7500021 | Software Architecture and Quality | Reussner | ||
| ST 2026 | 7500396 | Software Architecture and Quality | Reussner | ||
| WT 26/27 | 7500032 | Software Architecture and Quality | Reussner | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 Nr. 1 SPO) lasting 60 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (§ 6 Abs. 3 SPO) whether the
examination takes place
• in the form of an oral examination lasting approximately 45 minutes pursuant to § 4 Abs. 2 Nr. 2 SPO or
• in the form of a written examination lasting 60 minutes in accordance with § 4 Abs. 2 Nr. 1 SPO.
This lecture and the lectures Component-Based Software Development and Software Architecture are mutually exclusive.
| Coordinators: |
Prof. Dr.-Ing. Anne Koziolek
Prof. Dr. Raffaela Mirandola
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107235 - Software Engineering II |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 24076 | Software Engineering II | 4 SWS | Lecture / 🗣 | Reussner, Mirandola, Dehghani |
| Exams | |||||
| ST 2026 | 7500207 | Software Engineering II | Reussner | ||
| WT 26/27 | 7500054 | Software Engineering II | Reussner | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
None.
The course Software Engineering I should already have been attended.
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107212 - Software Product Line Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400050 | Software Product Line Engineering | 2 SWS | Lecture / Practice / 🗣 | Feichtinger |
| Exams | |||||
| ST 2026 | 7500280 | Software Product Line Engineering | Feichtinger | ||
| WT 26/27 | 7500158 | Software Product Line Engineering | Feichtinger | ||
The assessment is carried out as an oral examination, usually lasting 25 minutes in accordance with Section 4 (2) No. 2 SPO.
Depending on the number of attending students, it will be announced six weeks before the examination (§ 6 Para. 3 SPO) whether the performance assessment will take place
- in the form of an oral examination in accordance with Section 4 (2) No. 2 SPO (as described above) or
- in the form of a written examination lasting 90 minutes in accordance with Section 4 (2) No. 1 SPO.
None.
Basic knowledge from the lectures Software Engineering II [T-INFO-101370] and Formal Systems [T-INFO-101336] is helpful.
| Coordinators: |
Dr. Christopher Gerking
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-106344 - Software Security Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400059 | Software Security Engineering | 2 SWS | Lecture / 🗣 | Gerking |
| Exams | |||||
| ST 2026 | 7500357 | Software Security Engineering | Gerking | ||
| WT 26/27 | 7500040 | Software Security Engineering | Gerking | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting approx. 90 minutes.
None.
Knowledge of Software Engineering I and Software Engineering II is recommended.
| Coordinators: |
Prof. Dr. Ralf Reussner
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100719 - Software-Evolution |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424164 | Software Evolution | 2 SWS | Lecture / 🗣 | Lange |
| Exams | |||||
| ST 2026 | 7500023 | Software-Evolution | Reussner | ||
| WT 26/27 | 7500004 | Software-Evolution | Reussner | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 25 minutes.
None.
Knowledge of software technology and software architectures is helpful.
| Coordinators: |
Prof. Dr.-Ing. Ina Schaefer
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107239 - Software Test and Quality Management (SQM) |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400112 | Softwaretest and Quality Management (SQM) | 2 SWS | Lecture / 🗣 | Schaefer |
| WT 26/27 | 2400113 | Softwaretest and Quality Management (SQM) - Exercise | 2 SWS | Practice / 🗣 | Schaefer |
| Exams | |||||
| ST 2026 | 7500180 | Software Test and Quality Management (SQM) | Schaefer, Heydari Tabar | ||
| WT 26/27 | 7500248 | Software Test and Quality Management (SQM) | Schaefer | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
None.
At the end of the course there is also the opportunity to be certified as an "ISTQB - Certified Tester - Foundation Level". A date and the modalities for the exam will be agreed on in the lecture.
| Coordinators: |
Prof. Dr.-Ing. Uwe Hanebeck
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107847 - Stochastic Information Processing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424113 | Stochastic Information Processing | 3 SWS | Lecture / Practice / 🗣 | Hanebeck |
Success is assessed in the form of an oral examination, usually lasting 15 - 25 minutes in accordance with Section 4 (2) No. 2 SPO.
None.
Knowledge of the basics of probability theory is helpful.
As a theoretical foundation lecture, "Stochastic Information Processing" is an ideal introduction to the ISAS lectures. Conversely, prior knowledge of "Localization of Mobile Agents" (LMA) [course no. 24613] may nevertheless be helpful depending on the type of learner - more concrete applications are highlighted there. In all of our lectures, the content is always derived and explained in detail from the beginning; it is therefore possible to start with SI or LMA.
| Coordinators: |
Prof. Dr.-Ing. Eric Sax
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-100537 - Systems and Software Engineering |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2311605 | Systems and Software Engineering | 2 SWS | Lecture / 🗣 | Sax |
| WT 26/27 | 2311607 | Tutoral for 2311605 Systems and Software Engineering | 2 SWS | Practice / 🧩 | Stroop, Hagel, Gerdes |
| Exams | |||||
| ST 2026 | 7311605 | Systems and Software Engineering | Sax | ||
| WT 26/27 | 7311605 | Systems and Software Engineering | Sax | ||
Written exam of 90 minutes.
Students are given the opportunity to earn a grade bonus through separate task assignments. If the grade of the written exam is between 4.0 and 1.3, the bonus improves the grade by a maximum of one grade level (0.3 or 0.4). The exact criteria for awarding a bonus will be announced at the beginning of the lecture. Bonus points do not expire and remain valid for exams taken at a later date:
The grade is determined by the written exam and the bonus points.
none
| Coordinators: |
Prof. Dr.-Ing. Jürgen Bortolazzi
|
|---|---|
| Organisation: |
KIT Department of Electrical Engineering and Information Technology |
| Part of: |
M-ETIT-100462 - Systems Engineering for Automotive Electronics |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2311642 | Systems Engineering for Automotive Electronics | 2 SWS | Lecture / 🧩 | Bortolazzi |
| ST 2026 | 2311644 | Tutorial for 2311642 Systems Engineering for Automotive Electronics | 1 SWS | Practice / 🗣 | Beck |
| Exams | |||||
| ST 2026 | 7311642 | Systems Engineering for Automotive Electronics | Bortolazzi | ||
| WT 26/27 | 7311642 | Systems Engineering for Automotive Electronics | Bortolazzi | ||
none
| Coordinators: |
Prof. Dr. Martina Zitterbart
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107243 - Telematics |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400007 | Telematics | 4 SWS | Lecture / 🗣 | Zitterbart, Helmig, Mahrt, Seehofer |
| Exams | |||||
| ST 2026 | 7500098 | Telematics | Zitterbart | ||
| WT 26/27 | 7500166 | Telematics | Zitterbart | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 90 minutes.
Depending on the number of participants, it will be announced six weeks before the examination (Section 6 (3) SPO) whether the assessment will take place
None.
Offered for the last time in WS 26/27!!!!!
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-100851 - Testing Digital Systems I |
| Exams | |||||
|---|---|---|---|---|---|
| ST 2026 | 7500008 | Testing Digital Systems I | Tahoori | ||
| WT 26/27 | 7500039 | Testing Digital Systems I | Tahoori | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of Digital Design and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr. Mehdi Baradaran Tahoori
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-102962 - Testing Digital Systems II |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400014 | Testing Digital Systems II (findet im SS 2026 nicht statt) | 2 SWS | Lecture / 🖥 | Tahoori |
| Exams | |||||
| ST 2026 | 7500069 | Testing Digital Systems II | Tahoori | ||
| WT 26/27 | 7500147 | Testing Digital Systems II | Tahoori | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
Knowledge of Digital Design and Computer Architecture is helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107202 - Text Indexing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400005 | Text-Indexing | 3 SWS | Lecture / 🗣 | Kurpicz |
| Exams | |||||
| WT 26/27 | 7500537 | Text-Indexing | Sanders | ||
The assessment consists of an oral exam (generally 15 minutes) according to § 4 Abs. 2 Nr. 2 SPO.
The examination takes place in the form of an oral examination and a project/experiment as an examination of success of a different kind.
Weighting: 80% oral examination, 20% project/experiment. An overall grade is awarded.
None.
The lecture builds on parts of the contents of the lectures Algorithms I and Algorithms II. Corresponding knowledge is therefore helpful.
| Coordinators: |
Prof. Dr. Peter Sanders
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107202 - Text Indexing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2400005 | Text-Indexing | 3 SWS | Lecture / 🗣 | Kurpicz |
| Exams | |||||
| WT 26/27 | 7500145 | Text-Indexing Project/Experiment | Sanders | ||
The assessment is carried out in form of course work (German Studienleistung, § 4 Abs. 3 SPO). Students must regularly submit exercise sheets. The number of exercise sheets and the scale for passing will be announced at the beginning of the course. The assessment an only be repeated once.
The examination takes place in the form of an oral examination and a project/experiment as an examination of success of another type.
Weighting: 80% oral examination, 20% project/experiment. An overall grade is awarded.
None.
The lecture builds on parts of the contents of the lectures Algorithms I and Algorithms II. Corresponding knowledge is therefore helpful.
| Coordinators: |
Prof. Dr.-Ing. Marvin Künnemann-Goos
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107639 - Theory of Satisfiability Algorithms |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400077 | Theory of Satisfiability Algorithms | 2 SWS | Lecture / Practice / 🗣 | Künnemann-Goos |
| Exams | |||||
| ST 2026 | 7500410 | Theory of Satisfiability Algorithms | Künnemann-Goos | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting approx. 20-30 minutes.
None.
Basic knowledge of algorithms and data structures (e.g., Algorithms 1) and theoretical computer science (e.g., TGI) is assumed.
Basic knowledge in the analysis of randomized algorithms may be helpful (e.g., Probability and Computing), but is not a strict requirement.
| Coordinators: |
Prof. Dr. Sebastian Erdweg
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107664 - Type Systems and Program Analysis |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400188 | Type Systems and Static Program Analysis | 2 SWS | Lecture / 🧩 | Erdweg, Lego, Greule |
| ST 2026 | 2400192 | Exercises for the lecture Type Systems and Static Program Analysis | 2 SWS | Practice / 🧩 | Erdweg, Lego, Greule |
| Exams | |||||
| ST 2026 | 7500304 | Type Systems and Program Analysis | Erdweg | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 120 minutes.
None.
Knowledge of programming semantics is helpful but not required.
Knowledge of logic inference systems is helpful but not required.
| Coordinators: |
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107161 - Ubiquitous Computing |
| Courses | |||||
|---|---|---|---|---|---|
| WT 26/27 | 2424146 | Ubiquitous Computing | Lecture / Practice | Beigl | |
| Exams | |||||
| ST 2026 | 7500368 | Ubiquitous Computing | Beigl | ||
| WT 26/27 | 7500342_06.02.2026 | Ubiquitous Computing | Beigl | ||
| WT 26/27 | 7500367_07.11.2025 | Ubiquitous Computing | Beigl | ||
| WT 26/27 | 7500372_05.02.2026 | Ubiquitous Computing | Beigl | ||
The assessment is carried out as an oral examination (§ 4 Abs. 2 Nr. 2 SPO) lasting 20 minutes.
None.
| Coordinators: |
TT-Prof. Dr. Barbara Bruno
Dr.-Ing. Caterina Neef
Utku Norman
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107834 - User Studies in Human-Robot Interaction |
The assessment is carried out as an examination of another type (§ 4 Abs. 2 No. 3 SPO) in the form of a supervised, project‑based empirical user study in Human-Robot Interaction, covering study design, execution, analysis, and reflection.
The final grade is based on a written research-oriented report in conference paper format and an oral presentation.
The completion of intermediate project milestones during the semester is required as a prerequisite for admission to the final assessment.
The project‑based assessment counts for 100% of the final grade.
The assessment can only be repeated once.
Students may withdraw from the examination during the first four weeks after they have been assigned their chosen project topic.
None.
Participation in the lecture “HRI and Social Robotics” or similar introductory courses is recommended but not required. Familiarization with the lecture content is recommended if the course has not been attended.
Attendance is mandatory, as active participation in discussions, group work, and presentations is required.
| Coordinators: |
Prof. Dr.-Ing. Tamim Asfour
Prof. Dr.-Ing. Michael Beigl
|
|---|---|
| Organisation: |
KIT Department of Informatics |
| Part of: |
M-INFO-107113 - Wearable Robotic Technologies |
| Courses | |||||
|---|---|---|---|---|---|
| ST 2026 | 2400062 | Wearable Robotic Technologies | 2 SWS | Lecture / 🗣 | Asfour, Beigl |
| ST 2026 | 5016643 | BUT - Attractive Robot Technologies | Lecture / 🗣 | Asfour | |
| Exams | |||||
| ST 2026 | 7500219 | Wearable Robotic Technologies | Asfour, Beigl | ||
| WT 26/27 | 7500073 | Wearable Robotic Technologies | Asfour | ||
The assessment is carried out as a written examination (§ 4 Abs. 2 No. 1 SPO) lasting 60 minutes.