CSE 4309 - Machine Learning

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

• Most (but not all) Monday and Wednesday lectures will be held ONLINE and not face-to-face.
• Most (but not all) Friday lectures will be face-to-face.
• See the course schedule for the modality of each individual lecture.
• Students can attend every lecture (including face-to-face lectures) live using 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), Algorithms (CSE 3318), Probability (IE 3301), 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