2023-10-09 - 2023-10-13
Tristan Bereau
Heidelberg University
Generative learning represents one of the most exciting developments in machine learning (ML). Beyond regression or classification, these models produce data points according to an underlying distribution. We will explore recent developments in the context of molecular modeling and statistical mechanics. The course will not assume prior knowledge of ML, and will instead start with basic Bayesian inference. The topical content will follow with an exposure on molecular representations and physical symmetries. Modern ML architectures will be covered, including variational auto-encoders and generative adversarial networks. Finally, we will explore the use of normalizing flows for molecular simulations and free-energy calculations.
