A general introduction to machine learning that covers the theoretical foundations of various supervised and unsupervised learning techniques, as well as principled methods for assessing and comparing models. Emphasis is placed on practical applications via programming assignments in a high-level language.
Required Prerequisite Courses
Semester Offered
Spring, Even-Numbered Years Only