The aim of this graduate-level course is to describe the mathematical aspects of modeling high-dimensional data, with an emphasis on computational and statistical foundational questions. Topics include probabilistic graphical models, variational inference, MCMC methods, optimal transport, tools from statistical physics, and generative modeling using neural networks.
Lectures: Wednesdays at 10:00-11:40am ET - 19 University Place Room 102
To join via Zoom please send an email before the lecture and we’ll send a link
Recitations: Mondays at 11:15am-12:05pm ET - 60 Fifth Ave Room 150
Brightspace (for assignments and grades): https://brightspace.nyu.edu/d2l/home/224620
Instructor Office Hour: Wednesdays 3.30pm - 4.30pm - 60 Fifth Ave Room 600
TA Office Hour: Mondays 10am - 11am - 60 Fifth Ave Room 244
Lecture Instructor:
TA: