Course: STAT 37792
Title: Topics in Deep Learning: Generative Models
Instructor(s): Yali Amit
Class Schedule: Sec 01: This class is listed as MW 11:30 AM–1:30 PM on the Registrar's site, but it will actually meet MW 12:10 PM-1:30 PM.
Description: This course will be a hands on exploration of various approaches to generative modeling with deep networks. Topics include variational auto encoders, flow models, GAN models, and energy models. Participation in this course requires familiarity with pytorch and a strong background in statistical modeling. The course will primarily consist of paper presentations. Each presenter would be required to report on experiments performed with the algorithm proposed in the paper, exploring strengths and weaknesses of the methods.
Prerequisite(s): STAT 34300, STAT 34700, STAT 34800, and STAT 37601/CMSC 25025, or STAT 37710/CMSC 35400
