Prerequisites: CS 141, STAT 160A
Description: Covers methods for representing and reasoning about proba- bility distributions in complex domains. Focuses on graphical models and their extensions such as Bayesian networks, Markov networks, hidden Markov models, and dynamic Bayesian networks. Topics include algorithms for probabilistic inference, learning models from data, and decision making. May be taken Satisfactory (S) or No Credit (NC) by students advanced to candidacy for the Ph.D.