Prerequisites: CS 171 or EE 142 or CS 224; graduate standing
Description: For the CS 229 online section; enrollment in the Online Master-in-Science in Engineering program; graduate standing. A study of supervised machine learning that emphasizes discriminative methods. Covers the areas of regression and classification. Topics include linear methods, instance-based learning, neural networks, kernel machines, and additive models.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor. Credit is awarded for one of the following CS 229 or EE 240.