Summer school

Summer School: Statistical Physics & Machine Learning => reduced to 11 days workshop

August 3, 2020 - August 14, 2020
The school is aimed primarily at the growing audience of theoretical physicists, applied mathematicians and colleagues from other computational fields interested in machine learning, neural networks, and high-dimensional data analysis. We shall cover basics and frontiers of high-dimensional statistics, machine learning, theory of computing and statistical learning, and the relevant mathematics and probability theory. We will put a special focus in particular on methods of statistical physics and their results in the context of current questions and theories related to these problems. The school will also cover some examples of applications of machine learning methods in physics research, as well as other emerging applications of wide interest. Open questions and directions will be presented as well.
Summer School organized by:

Gérard BEN AROUS, New York University, USA
Surya GANGULI, Standford University, USA
Florent KRZAKALA, LPS, ENS Paris, France
Lenka ZDEBOROVA, IPhT, CNRS and CEA Saclay, France

More information can be found on the Summer School's Website.

To download the School's Poster please click HERE.

The Applications are now closed.

Might be reduced to 11 days workshop, more information will be given by June 15th.

Updated on August 11, 2020