August 22 – 27 , 2021, Event 21343

Leibniz MMS Summer School


Christian Bayer (Weierstraß Institut – Berlin, DE)
Martin Eigel (Weierstraß Institut – Berlin, DE)
Nicole Mücke (TU Braunschweig, DE)
Feliks Nüske (Universität Paderborn, DE)
David Sommer (Weierstraß Institut – Berlin, DE)
Nikolas Tapia (Weierstraß Institut – Berlin, DE)
Jia-Jie Zhu (Weierstraß Institut – Berlin, DE)

For support, please contact

Heike Clemens


In the last years, modern Machine Learning (ML) and in particular Deep Learning (DL) methods, have revolutionized many application areas (e.g. image recognition, natural language processing, etc.) in research and industry. These methods have also started to be used in the field of scientific computing to speed up simulations, interpret measurement and simulation data and to recover dynamics.

In this school, fundamental and advanced aspects of ML methods are presented with the aim to enable the participants to apply such methods to their specific research problems. The topics that will be discussed include kernel methods and Gaussian processes, stochastic and robust optimization, deep Neural Networks. In addition to daily lectures, the participants will get the opportunity to become familiar with the presented methods in accompanying practical lab sessions.

Motivation text license
  Creative Commons BY 3.0 DE
  Christian Bayer and Martin Eigel

Online Publications

We offer several possibilities to publish the results of your event. Please contact publishing(at) if you are interested.

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf in the library.