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: Open to graduate students in the CSE program, others with consent. Recommended preparation: CSE 3500, MATH 2110Q, MATH 2210Q, and MATH 3160 or the equivalent.

Grading Basis: Graded

Enables students to understand and use machine learning methods across a wide range of settings. 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, introduces advanced topics such as dimension reduction; structured data learning; kernel learning; imprecisely supervised learning; longitudinal data analysis; causal inference, etc.


Last Refreshed: 09-AUG-22 05.20.17.316132 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2022 16644 Storrs In Person Bi, Jinbo 001 Reg TuTh 3:30pm‑4:45pm
27/30 3.00 Graded