CSE 250APrinciples 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. Recommended preparation: CSE 103 or similar course.
Prerequisites
From the catalog: “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.”
Courses that list CSE 250A as a prerequisite (1)
- LIGN 256 Statistical Natural Language Processing