Prerequisites: CS 100, STAT 155, MATH 031; graduate standing
Description: For the CS 224 online section: enrollment in the Online Master-in-Science in Engineering program; graduate standing. 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.
Credit: May be taken Satisfactory (S) or No Credit (NC) with consent of instructor and graduate advisor.