CS 598 BAN

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites. Course Information: May be repeated in the same or separate terms if topics vary.

Deep Generative and Dynamical Models Recent years have seen considerable advances in generative models, which learn distributions from data and also generate new data instances from the learned distribution; and dynamical models, which model systems with a dynamical or temporal component. Both of these developments have been leveraging advances in deep learning. The course will cover key advances in generative and dynamical models, including variational auto-encoders, normalizing flows, generative adversarial networks, neural differential equations, physics guided machine learning, among other topics. The course will be based on lectures, paper readings, presentations, and a course project. The course will assume the students have taken introductory courses in machine learning and deep learning equivalent to CS 446: Machine Learning and CS 498: Intro to Deep Learning. CS 225. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.il