CSE 4309 - Machine Learning

Fall 2023
Lectures: MWF 10:00am-10:50am
Modality: Hybrid.
Classroom for face-to-face lectures: NH 110.

Instructor: Vassilis Athitsos

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) or consent of instructor, Algorithms (CSE 3318), Probability (IE 3301 or MATH 3313), and Linear Algebra (CSE 3380 or MATH 3330).