August 23, 2021 - August 27, 2021
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.
- Christian Bayer (WIAS, organizer): Fundamentals of statistical learning theory and introduction to machine learning with python.
- Martin Eigel (WIAS, organizer): Fundamentals of statistical learning theory and introduction to machine learning with python.
- Nicole Mücke (TU Berlin): Distributed Learning
- Feliks Nüske (Paderborn University): Dynamical Systems
- Jia-Jie (JJ) Zhu (WIAS Berlin): Robust Optimization
Application for Participation
Participation including housing and meals is free for PhD students from network member institutions. Travel expenses for the journey to Dagstuhl have to be covered by the hosting Leibniz institutions.
Interested PhD students are requested to apply for participation before June 29, 2021.
Application form »