Computer Science 791B - S-Bayesian Deep Learning

This seminar will introduce students to research in the area of Bayesian methods applied to deep neural network models. The course will begin with foundational readings on Markov chain Monte Carlo and variational Bayesian methods and proceed to cover recent advances that are enabling the application of Bayesian inference to increasingly large deep learning models. The course will also cover methods for accelerating prediction using Bayesian deep learning models and for evaluating Bayesian deep learning models. Students will need background in deep learning (such as provided by COMPSCI 682 or COMPSCI 689) and probabilistic graphical models (such as provided by COMPSCI 688). The seminar will focus on reading, presenting, and discussing classical and recent papers (1 credit) and a final project focusing on a Bayesian deep learning topic (3 credits).

Open to Graduate Computer Science students only. SECT 01=3 CR; SECT 02=1 CR.