CSE250A - Principles of Artificial Intelligence: Probabilistic Reasoning and Learning



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