CSE 616 Pattern Recognition and Machine Learning
(4 credits)
Description:
Introduction to recognition and learning; Bayes decision theory;
parametric and nonparametric methods including Hidden Markov models;
Discriminant functions including support vector machines; Multilayer
neural networks; Decision and regression trees for learning; Performance
estimation; Unsupervised learning and clustering; Subspace methods;
Application.
Prerequisites: CSE 506 and CSE 507 or equivalent.