Lectures: MWF 10:00am-10:50am
Classroom for face-to-face lectures: TBD.
This course offers an introduction to machine learning. Topics include naive Bayes classifiers, linear regression, linear classificiers, neural networks and backpropagation, kernel methods, decision trees, clustering, and reinforcement learning. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics. Prerequisites: Admitted into an Engineering Professional Program, and C or better in each of the following: Calculus III (MATH 2326), Algorithms (CSE 3318), Probability (IE 3301), and Linear Algebra (CSE 3380 or MATH 3330).