In this class, we will explore how the concepts of statistical physics can prove useful to tackle main theoretical questions in machine learning and how the recent progress in generative AI can assist statistical physics computations. We will describe analytical approaches as well as numerical approaches at this intersection. We will present the methods of statistical physics and high-dimensional probability which play a central role in these recent and new research activities, and also make an incursion in some other interdisciplinary applications (biological neural networks and theoretical ecology). 

Some TDs will illustrate the lecture’s topic by analytical computations, others by numerical implementations in python and pytorch.

Wednesday afternoon: Lecture 2pm-3.45pm + TD 4-5:30pm  

This course will explore how living systems make use of circuits and networks to process information from neuroscience to synthetic biology.

The goal of this course is to better understand how interfaces instabilities can affect morphogenesis. 

Quantum Field Theory (QFT) is one of the cornerstones of modern physics, encompassing Quantum Mechanics and Relativity in an unique coherent framework, providing the logical foundations  of high energy physics. QFT has found applications in almost any branch of modern theoretical physics, from particle physics to cosmology, from gravity to statistical mechanics,  and even beyond.