Lecturers

Main lecturers: Pr. Florent Krzakala, head of IdePHICS lab (with help from the amazing Pr. Lenka Zdeborova, head of SPOC lab)

Teaching assitants: Yatin Dandi, Guillaume Dalle, Luca Pesce.

Main topics

Interest in the methods and concepts of statistical physics is rapidly growing in fields as diverse as theoretical computer science, probability, machine learning, discrete mathematics, optimization and compressed sensing. This course will cover this rich and active interdisciplinary research landscape.

More specifically, we will review the statistical physics approach to problems ranging from graph theory (percolation, community detection) to discrete optimization and constraint satisfaction (satisfiability, coloring) and to machine learning problems (learning with two layer neural networks, mean-field dynamics, Gradient-Descent and SGD, low-rank matrix and tensor factorization, etc.).

We will expose theoretical methods of analysis (replica, cavity, …) algorithms (message passing, spectral methods, …), neural networks (theory of SGD, …) discuss concrete applications, highlight rigorous justifications as well as present the connection to the physics of glassy and disordered systems.

The aim is to make this growing field accessible to students from computer science, math, and engineering, not only physics (but of course, physicists are more than welcome). The course is set up so that we shall give open problems during and at the end of the semester.

References: Information, Physics and Computation (Oxford Graduate Texts), 2009, M. Mézard, A. Montanari

Statistical Physics of inference: Thresholds and algorithms, Advances in Physics 65, 5 2016, L. Zdeborová & F. Krzakala

Times and Place

Main lectures will on Friday, from 10:00-12:00 in CM011 and exercices will be on Thursday, 10:00-12:00

Recorded lectures will be avalible on mediaspace click here

Detailed lectur notes (in construction):

Lecture notes: Chapter 1-13