Principles of Artificial Intelligence: Probabilistic Reasoning and Learning

4 credits

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. Recommended preparation: CSE 103 or similar course. Prerequisites: graduate standing. Restricted to students within the CS75, CS78, CS89, CS88, CS77, CS80, CS81, CS83, and CS76 majors. All other students will be allowed as space permits. Recommended preparation: understanding of probability and statistics.