This page refers to the Spring 2017 offering of CSE 574 only. The information on this page does not necessarily apply to every offering of CSE 574.
Introduction to Machine Learning
Involves teaching computer programs to improve their performance through guided training and unguided experience. Takes both symbolic and numerical approaches. Topics include concept learning, decision trees, neural nets, latent variable models, probabilistic inference, time series models, Bayesian learning, sampling methods, computational learning theory, support vector machines, and reinforcement learning.
CSE 250 and any of EAS 305/308, STA 401/421, MTH 309; or permission of instructor.
Ph.D.: This course fulfills one Artificial Intelligence Core Course (Breadth) requirement.
M.S.: This course fulfills one Artificial Intelligence Core Course (Breadth) requirement.