MLS Lab · University of Stuttgart
Courses, seminars, theses, and research projects at MLS.
The listings below show the most recent 2026 offerings in the institute directory. Timetables, enrolment, and module requirements are maintained in C@mpus. WS denotes winter semester; SS denotes summer semester.
A seminar on deep learning applications in science and engineering. Students read and discuss research papers, prepare a presentation, and write a report. Prior coursework in machine learning, reinforcement learning, or related areas is expected.
Topics include search, constraint satisfaction, game playing, logic-based agents, probabilistic reasoning, decision-making, and an introduction to machine learning and deep learning.
A departmental colloquium for staff and doctoral researchers.
Lecture and exercise offerings in the MLS teaching programme. Consult the linked course records for module requirements and registration.
A departmental colloquium for staff and doctoral researchers.
A seminar listed in the MLS teaching programme. Topic selection and registration details are provided through the university systems.
The course covers Markov decision processes, model-free and model-based learning, policy gradients, Q-learning, offline and inverse reinforcement learning, and selected deep reinforcement learning methods.
Information reviewed 26 September 2026 · Institute teaching directory