CSE 4309 - Machine Learning

Fall 2024
Lectures: MWF 10:00am-10:50am
Modality: Hybrid.
Classroom: NH 108.

• The plan is for all lectures to be held both face-to-face (at NH 108) and online. Any exceptions will be announced via e-mail and on Canvas.
• Students can choose how to attend every lecture. Options include attending in person at NH 108, attending live using Microsoft Teams, or viewing an offline recording of the lecture on Microsoft Teams.

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).

Links

• Course Information and Policies: You can treat this information as an unofficial version of the syllabus. The official version is posted on Canvas.

• Schedule and Lecture Slides

• Assignments