CSE 616 - Pattern Recognition and Machine Learning

(4 credits)

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.

Prerequisite(s): Students must meet prerequisites (CSE 506 and CSE 507 or equivalent).

Course revisions made after the Graduate Catalog publication date will be posted in the Graduate Catalog Addendum.