This two-year, English-taught master's programme builds a strong theoretical foundation in Artificial Intelligence and Data Science while emphasizing practical solutions to real-world problems. Core teaching centers on machine learning—especially deep learning—and covers both the mathematical and statistical principles that underpin contemporary AI research. Throughout the course you will study specialised topics such as natural language processing, computer vision, and the analysis of biological and medical data, linking theory to application.
The curriculum is flexible: a range of elective courses allows you to shape the degree around your interests and career goals. Graduates leave with a solid grasp of state-of-the-art algorithms and the ability to apply them to specific research questions or industrial challenges. The programme also prepares you for research roles in academia or R&D departments by developing the skills needed to design and evaluate data-driven solutions.
There are no tuition fees for the programme; students pay a semester contribution of approximately €300. For details on application deadlines, specific entry criteria, and required documents, consult the university’s official programme page.
Key facts / practical information
This master's curriculum combines a focused set of core courses in algorithms, machine learning and statistics with practical research training and a flexible elective portfolio. The required core modules build the theoretical and programming foundations you need to design, analyse and implement AI and data-driven systems, while lab rotations and the thesis give extended hands-on experience in current research topics. Electives let you tailor the degree toward areas such as applied machine learning, data engineering, or advanced AI methods.
Key learning outcomes include advanced algorithmic problem solving and software development, solid grounding in the mathematical and statistical principles used in data science, practical experience with contemporary deep learning architectures, and the ability to carry out an independent research project and communicate results professionally. The lab rotations are designed to immerse you in research groups and help you choose a thesis direction, and the thesis seminar supports the development of scientific presentation and discussion skills.
(Note: CP = credit points, commonly used to indicate ECTS credits in German programmes.)
This master’s track has restricted admission, so places are limited and meeting the formal entry criteria does not automatically guarantee a spot. Admission requires a first university degree with the specific subject-related background defined by the programme’s exam regulations. The programme’s examination board reviews each application to decide whether your prior studies provide the required preparation.
Make sure your application clearly documents course contents, ECTS credits and the level at which relevant modules were taught, because the exam board will use that information to assess eligibility. Your final Bachelor’s grade must meet the German minimum threshold (see below); if your degree was awarded outside Germany, convert your grade to the German scale using the Bavarian Formula before submission.
Admission requirements (bullet points)
Tip for applicants: include detailed transcripts and short module descriptions (content and ECTS) to help the exam board evaluate level and equivalence.
Winter Semester (International)
15 July 2026
Winter Semester (EU/EEA)
15 July 2026
Graduates are prepared for careers in academic research, industry R&D, and data-driven engineering roles. The combination of mathematical/statistical foundations, practical lab rotations and a research-focused thesis equips students to design, implement and evaluate state-of-the-art AI solutions in areas such as healthcare analytics, autonomous systems, natural language applications, and computer vision.
Alumni can pursue positions as machine learning engineers, data scientists, research scientists in industrial labs, or continue on to doctoral studies. The programme's research links and practical placements also help graduates transition into R&D teams at companies or research centres.
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