The University of Tübingen sets up an international program of study with a research emphasis on artificial intelligence.
“The University of Tübingen has made societally responsible and relevant research a maxim. We want to educate experts who understand not only the potential of the new technology, but also the risks it poses, i.e. experts who are able to consider them, and minimise those threats.”
Tübingen is the first German university to start a master’s program in machine learning in the coming winter semester. Students from all around the world can apply to take part in this four-semester program. The language of instruction is English. The program teaches the basics of the subject and allows a choice of specialisation in a broad range of theory and applications. When it comes to machine learning, Tubing is a leading global location. It offers many opportunities, even early in the program, to take part in the latest research.
Whether it is about Internet advertising or personalized medicine, autonomous learning machines make more than headlines – they are also already influencing our everyday life. Their use paves the way for major breakthroughs, for instance, in vehicle safety or improved cancer treatments. The field also provides researchers with new insights into the secrets of life and outer space. Machine learning is the study of algorithms which can gather “experiences” from data in order to identify patterns and regularities. As a result, it is one of the main drivers of current progress in the field of artificial intelligence (AI).
The research-oriented study program is closely linked to the local research hub at the university. It gives students an opportunity to learn about applications in information technology, and related fields such as computer vision, bioinformatics, the neurosciences, medical informatics, the cognitive sciences, linguistics or robotics. “Things are developing really rapidly right now in machine learning. What is a very promising approach today could already be passé tomorrow,” explains computer scientist Ulrike von Luxburg. “That’s why we have made the course of study as flexible as possible. There are few required courses. Students are free to set their priorities,” she adds. They can also acquire interdisciplinary qualifications, for example through lectures in law, philosophy, or ethics.
As one of the spokeswomen of the new cluster of excellence “Machine Learning: New Perspectives for Science,” Luxburg, together with her colleagues Matthias Hein, Philipp Hennig, and Kay Nieselt, has taken the initiative to incorporate the booming field with an instructional offer in the form of a Master’s program. “The University of Tübingen has made societally responsible and relevant research a maxim. We want to educate experts who understand not only the potential of the new technology, but also the risks it poses, i.e. experts who are able to consider them, and minimize those threats,” says Philipp Hennig. “Intelligent machines are going to change the world. We may not leave this process to others. Our students should be able to shape this process for the general good,” he adds.
A solid foundation in mathematics is a prerequisite for students, explains Nieselt, the dean of studies in the Department of Computer Science, where the Master’s program will be based. “It’s not necessarily required to have a Bachelor’s degree in Computer Science, Mathematics, or Physics. The main thing is knowing the basics: programming, algorithms, data structures, and lots and lots of mathematics,” she says. Because the language of instruction for the international course of study is exclusively English, adds Nieselt, a corresponding level of proficiency in the language is also a prerequisite.
Tübingen is one of Germany’s leading research locations for machine learning. Around 40 percent of the German scholarly publications on the topic originate from the Swabian university town. The University of Tübingen is part of the research association Cyber Valley and the site of the Machine Learning Competence Centre, which is supported by Germany’s Federal Ministry of Education and Research.