Non-equilibrium physics of machine learning – Overview

LSF Course

Contents

The recent advances in artificial intelligence rely on large machine lerning models in the form of deep neural networks. In this lecture, we will study how and why such models work using tools from non-equilibrium statistical physics.To this end, we will treat neural networks as complex systems consisting of interacting agents and study collective phenomena thereof. The lecture will start with a general introduction to non-equilibrium physics, including stochastic processes, and machine learning. We will then introduce theoretical concepts from the theory of disordered systems and apply them to deep learning.

Requirements

• Statistical physics
• Programming