# Overview

In the last decade, there have been an increasing convergence of
interest and methods between theoretical physics and fields as diverse
as probability, machine learning, optimization and compressed sensing.
In particular, many theoretical and applied works in statistical
physics and computer science have relied on the use of message passing
algorithms and their connection to statistical physics of glasses and
spin glasses. The aim of this school, especially adapted to PhD
students, post-docs, and young researchers, is to present the
background necessary for entering this fast developing field.

The school will cover background necessary to understand the
statistical physics and information theory techniques and methods, in
particular inference, computational complexity, message passing
algorithms, cavity and replica calculations (first week) as well as to
become familiar with the most important applications of these
techniques (second week). An important aim is to attract audience not
only from physics but also from combinatorics, probability, computer
science, machine learning and information theory.

Specific topics will include: