Mediaspace scheduled maintenance: Aug 25, 2026 07:00 - 12:00 AM. During this time, videos will be temporarily unavailable. Check status updates.

Graph
Search

Cours

PHYS-759: Nonequilibrum Physics for Complex Systems

Résumé

We will discuss recent topics in nonequilibrium physics of complex living and artificial systems, ranging from collective behaviors in active matter to noise and information processing in cellular biophysics to stochastic learning dynamics in deep artificial neural networks.

Séances de cours

Enseignant·e

Sahand Jamal Rahi

Source officielle

https://edu.epfl.ch/coursebook/fr/nonequilibrum-physics-for-complex-systems-PHYS-759

Page Moodle

http://go.epfl.ch/PHYS-759

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Résumé

Séances de cours

Enseignant·e

Sahand Jamal Rahi

Source officielle

https://edu.epfl.ch/coursebook/fr/nonequilibrum-physics-for-complex-systems-PHYS-759

Page Moodle

http://go.epfl.ch/PHYS-759

À propos de ce résultat

Cours associés (31)

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practised. ...

CIVIL-226: Introduction to machine learning for engineers

Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinking education at EPFL, Civil students will learn ML with ...

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

CS-433: Machine learning

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and practically implemented. ...

CS-456: Deep reinforcement learning

This course provides an overview and introduces modern methods for reinforcement learning (RL.) The course starts with the fundamentals of RL, such as Q-learning, and delves into commonly used approaches, like PPO and DQN. The course will introduce student ...

Afficher plus