Methods based on probability theory for reasoning and learning under uncertainty. Content may include directed and undirected probabilistic graphical models, exact and approximate inference, latent variables, expectation-maximization, hidden Markov models, Markov decision processes, applications to vision, robotics, speech, and/or text. CSE 103 or similar course recommended.
graduate standing in CSE or consent of instructor.
Formerly CSE250A - Artificial Intelligence: Search and Reasoning - Revised Spring 2013