Machine Learning (MSc)

Overview

This English-language Master's in Machine Learning combines a broad range of machine-learning topics with general computer science, giving students a high degree of choice: aside from a small set of compulsory modules, you tailor your courses to match your interests. Teaching formats include lectures, seminars and project lab courses supervised by active researchers, who will introduce both foundational theory and applied, up-to-date research topics in machine learning.

Research-led education

The curriculum is closely integrated with Tübingen’s strong local research ecosystem in machine learning and computer science, drawing on expertise from institutions such as the Max Planck Institute for Intelligent Systems, the Max Planck Institute for Biological Cybernetics, the Excellence Cluster “Machine Learning: New Perspectives for Science,” the Tübingen AI Center, the ELLIS Institute, Cyber Valley, and the IMPRS for Intelligent Systems. Top researchers across the main methodological areas of machine learning contribute to teaching, and the programme is aligned with the latest developments and pressing questions in the field (see CSRankings for the University of Tübingen; the Max Planck Institute for Intelligent Systems is also based in Tübingen).

Projects and thesis

Hands-on project work and the Master’s thesis give you the chance to write code for research purposes and develop your own scientific projects. Emphasising interdisciplinarity, the Master’s thesis may be supervised by a professor from any subfield of computer science, enabling collaboration across methodological or application domains.

Key facts / requirements

• Language of instruction: English
• Field: Computer Science with a focus on Machine Learning
• Curriculum: small number of mandatory courses + electives chosen by students
• Teaching formats: lectures, seminars, project lab courses supervised by research staff
• Strong integration with local research institutions (listed above) and active research-led teaching
• Practical emphasis: project work and thesis involve developing research code and independent scientific projects
• Interdisciplinary option: thesis supervision possible from any computer-science subfield

Curriculum overview

This MSc program is structured around four broad study areas: Foundations of Machine Learning, Diverse Topics in Machine Learning, General Computer Science, and Expanded Perspectives. The curriculum is designed to build core theoretical knowledge first, then broaden into specialized and interdisciplinary topics. Course planning is typically spread across four semesters, with the final semester dedicated to the Master’s thesis.

Core modules recommended early in the course include Mathematics for Machine Learning, Deep Learning, and Data Literacy, which establish the mathematical tools, neural-network techniques, and practical data handling skills you’ll need. In the subsequent semester, the program emphasizes Statistical Machine Learning and Probabilistic Machine Learning to deepen your understanding of inference, uncertainty modelling, and principled statistical approaches. Students are encouraged to combine coursework with hands-on experience: at least one practical (lab or project) course and one seminar should be taken in the second or third semester to develop applied skills and presentation/research abilities.

Learning outcomes

Completing this curriculum prepares you to:

• Apply mathematical and statistical foundations to machine learning problems.
• Design and implement deep learning systems and data pipelines informed by data literacy practices.
• Use probabilistic methods for modelling uncertainty and making principled inferences.
• Integrate knowledge from general computer science and broader perspectives to address complex, interdisciplinary problems.
• Demonstrate applied research and communication skills through practical coursework, seminars, and a final Master’s thesis.

Key requirements (recommended)

• Semester 1: Mathematics for Machine Learning; Deep Learning; Data Literacy (recommended)
• Semester 2: Statistical Machine Learning; Probabilistic Machine Learning (recommended)
• Take at least one practical (project/lab) course and one seminar in Semester 2 or 3
• Master’s thesis recommended in Semester 4

These elements are intended to guide study planning so you graduate with both theoretical depth and practical experience suited for research roles or advanced industry positions in machine learning.

Graduates are prepared for research-oriented roles in industry and academia, including positions as machine learning engineers, research scientists, data scientists, and PhD candidates. The programme’s strong ties to leading research institutes and emphasis on project work and thesis development make graduates competitive for R&D roles in AI labs, startups, and tech companies.

The mathematical and computational foundations taught also support careers in adjacent sectors such as finance, bioinformatics, robotics, and healthcare technology, where advanced modelling and data-driven methods are in demand. Alumni pursuing academia benefit from the programme’s research integration and opportunities for collaboration with local institutes.