Prerequisites: CS 224; graduate standing
Description: Covers methods for representing and reasoning about probability 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.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.