Mathematical and Computational Aspects of Machine Learning - 10 October 2019

9:00- 10:00 Barbier, Jean
Mean field theory of high-dimensional Bayesian inference

10:00- 10:30 Coffee break

10:30- 11:30 Peyré, Gabriel
Optimal Transport for Data Science

11:30- 12:30 Peyré, Gabriel
Optimal Transport for Data Science

14:30- 15:30 Grohs, Philipp
Approximation theory, Numerical Analysis and Deep Learning

15:30- 16:00 Coffee break

16:00- 17:00 Grohs, Philipp
Approximation theory, Numerical Analysis and Deep Learning

17:00- 18:00 Grohs, Philipp
Approximation theory, Numerical Analysis and Deep Learning

Still, the conceptual mechanisms on which such forms of learning work are largely not understood. Moreover, there is a complete lack of prediction ability, not only in the efficiency of machine learning, but also on its ability to work at all on a new problem. All this calls for a strong commitment on the part of the mathematical community. The present school aims at connecting international experts at the forefront of research on the mathematical and computational aspects of the problem with the interested scholars, especially the young generations.