August 23, 2021 - August 27, 2021

Venue: Schloss Dagstuhl - Leibniz-Zentrum für Informatik GmbH

Symbol Image Machine Learning
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.


Symbol Image Machine Learning
Speakers:
  • 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 »



Image copyrights:

  • header image and lower figure left (back propagation example) under license CC BY 4.0 (header image slightly fomally modified)
  • drawing 1st paragraph: WIAS
  • lower figure right (artificial neural network with chip) under license CC BY 2.0