Statistical and Biological Physics

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Non-equilibrium physics of machine learning

The recent advances in artificial intelligence rely on large machine-learning 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.