Graduate Course Descriptions

The following directory lists the graduate courses which the University expects to offer, although the University in no way guarantees that all such courses will be offered in any given academic year, and reserves the right to alter the list if conditions warrant. Click on the links below for a list of courses in that subject area. You may then click “View Classes” to see scheduled classes for individual courses.

5820. Machine Learning

3.00 credits

Prerequisites: Prerequisite: Open to graduate students in the CSE program, others with permission. Recommended preparation: CSE3500, MATH2110, MATH2210, and MATH3160 or the equivalent. (RG5909)

Grading Basis: Graded

The objective of the course is to enable students to understand and use machine learning methods across a wide range of settings. The course will be a mixture of theory, algorithms, and hands-on projects with real data. Besides traditional machine learning topics, e.g., supervised learning, unsupervised learning and semi-supervised learning, the course will also introduce advanced topics such as dimension reduction; structured data learning; kernel learning; imprecisely supervised learning; longitudinal data analysis; causal inference, etc.

No classes found.