Target Audience：Undergraduate/Graduate students
This course introduces how to develop deep generative models (DGMs) by integrating probabilistic graphical models and deep learning to generate realistic data including images, texts, graphs, etc. Course contents include 1) basics of probabilistic graphical models, including Bayesian network and Markov random field; 2) posterior inference methods, including message passing, variational inference, and Markov chain Monte Carlo sampling; 3) parameter learning and structure learning methods, including maximum likelihood estimation, expectation–maximization algorithm, and graphical LASSO; 4) deep generative models (DGMs), including variational auto-encoder, generative adversarial networks, normalizing flows, and evaluation of DGMs; 5) applications of DGMs in image generation, text generation, and graph generation.
Machine Learning, Probability and Statistics
Dr. Pengtao Xie's research interest is machine learning. He obtained his PhD in computer science from Carnegie Mellon University. He has published 50+ papers at top conferences and journals including ICML, NeurIPS, CVPR, ACL, etc. He was recognized as a top-5 finalist for the AMIA Doctoral Dissertation Award, Siebel Scholar, etc. He serves as area chairs for ICML, NeurIPS, etc.