Prerequisites: CS 100; STAT 155 or EE 114; MATH 031; For the CS 224/EE 242A online section: enrollment in the Online Master-in-Science in Engineering program; graduate standing
Description: ; graduate standing; or consent of instructor. A study of generative and discriminative approaches to machine learning. Topics include probabilistic model fitting, gradient-based loss optimization, regularization, hyper-parameters, and generalization. Includes experience with data science programming environments, data from practice, and performance metrics.
Cross-listing: Cross-listed with EE 242A.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.