Prerequisites: CS 171 or EE 142 or CS 224 or EE 242A; For the CS 229/EE 242B online section; enrollment in the Online Master-in-Science in Engineering program; graduate standing; graduate standing
Description: 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.
Cross-listing: Cross-listed with EE 242B.
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, EE 242B, or EE 240.