Degree Programs

Study programs are listed under their official names (usually in German, English in some cases) titles on the left. WS/SS indicates whether the program commences in the winter or summer semester.

Machine Learning - Master

Key data

Mathematisch-Naturwissenschaftliche Fakultät
Target degree


Restriction on admission


Regular duration of study

4 semester

Admission semester


Degree type


Language of instruction

English only

Our International Master's program in Machine Learning is about to open its doors! The first courses will be taught in winter term 2019/2020.

Study content

The international Master’s program in machine learning offers a wide choice of courses in computer science and, apart from a few mandatory courses, allows students to choose their subjects according to interest. Students will attend lectures, seminars, and project lab courses under the supervision of scientists who introduce them to basic and applied research and current topics in machine learning.

To pick up on scientific trends and make the best use of the current state of research, the curriculum relies heavily on the strong research presence on site, in machine learning as well as the wider field of computer science: top-level researchers in all major methodological branches of machine learning are present in Tübingen – personnel that will actively engage in teaching for the Master’s Program Machine Learning. Since the field is obviously very young and currently developing extremely rapidly, training will naturally be based on the most recent insights and the most pressing research questions of these teaching researchers. Project work and the Master’s thesis will offer students the opportunity to develop code for research purposes and their own scientific projects.

As interdisciplinarity is an important aspect, the Master’s thesis can be supervised by a professor any subfield of computer science.

Selection procedure

Admission to the international Master’s program in Machine Learning is not admission-restricted, but all students, including those holding a Bachelor’s degree in Computer Science, are requested to submit a formal application.

Entrance requirements

A Bachelor's degree (or equivalent) in either computer science, mathematics, physics or related natural sciences with a grade of at least 2.3 (German grading system). In particular, competences from the following areas are required, equivalent in content and scope to those in the BSc course in Computer Science in Tübingen:

  • Mathematics: one- and multidimensional analysis, linear algebra and either numerics or stochastic
  • Computer science: Programming, Algorithms and Data Structures.

Applicants must also provide adequate proof of English proficiency.

Application deadline for international (non-EU) citizens as well as German and EU citizens: 30 April

Language requirements

Courses are held in English. Applicants must provide adequate proof of English proficiency, documented by one of the following documents:

  • German Abitur certificate with proof of 6 (G8) or 7 years (G9) of English language instruction
  • TOEFL iBT test with at least 94 points
  • IELTS test with a score of at least 7.0
  • Cambridge Certificate in Advanced English (CAE)
  • Higher education entrance qualification from Great Britain, Ireland, USA, Canada, Australia, New Zealand

Possible Combinations

The Machine Learning program cannot be complemented with a minor subject.

Career perspectives

Due to very fast technological developments in handling large amounts of data and apply findings in a wide variety of applications, there is an ever growing need for specialists in machine learning. Since this is a highly demanding area, a PhD degree is often required. Graduates in this international Master's program will be competent in all basic and many advanced areas of machine learning, understanding and suitably applying this increasingly essential tool for dealing with large datasets, be it in science, industry or alternative domains. The studies program deals both with generic methods and their applications to specific fields, making it highly relevant for new career and job market purposes. In this whole Master program, besides professional expertise, graduates will also acquire language skills and intercultural competence due to the program’s international nature – another requirement of the international job market, both in academia and without.

PhD Possibility

Depending on the course selection, the master program qualifies for doctoral studies in Computer Science. For further information see the website Faculty of Science - Doctoral Studies.

Please click on the following link for further information concerning induction sessions taking place at the beginning of the semester: How to get started - general information for new students

Here you can find the contact persons within the Department of Computer Science.

Course Counselling

If you have questions concerning your application or your admission, please contact the Dean of Studies:
Prof. Dr. Kay Nieselt

If you have any questions concerning the process or organisation of your studies, please contact the students' advisory service:
studienberatungspam prevention@informatik.uni-tuebingen.de

The examination administration is carried out by the Central Registrar´s Office of the University of Tübingen. Here you cand find the contact partners and their contact details.

Study Outline

The course consists of four major study areas: Foundations of Machine Learning, Diverse Topics in Machine Learning General Computer Science and Expanded Perspectives. In the first semester Mathematics for Machine Learning, and Data Literacy are recommended modules, followed by Statistical Learning, Probabilistic Inference and Learning and Deep Learning in the next semester. At least one practical course and one seminar are recommended for the second or third semester. The master thesis is recommended for the fourth semester.

The Master’s program in machine learning is research-oriented and will enable graduates to analyze, implement, leverage, and modify techniques of machine learning. Education in problem solving capabilities is a central training objective.

Students have the opportunity to advance their knowledge and skills to a level which will allow them to get involved in top national and international research in bioinformatics and its application to the life sciences. This includes a critical discussion of research goals, contents, proposals, and research problems, and requires a high scientific level.

You can find a formal description of the degree here.

Erasmus-Programme (Uppsala, Schweden)

Information concerning the exchange programmes of the University: International Office 

Departmental student council

The student association is involved in many organizational issues. Furthermore it offers an easy and informal way to come together and get in touch with other students.


Informatik, Maschinelles Lernen, künstliche Intelligenz