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.

5602. Machine Learning for Physical Sciences and Systems

Also offered as: CSE 5602

3.00 credits

Prerequisites: Open to graduate students in Computer Science and Engineering, MEng in Advanced Systems Engineering, and MEng in Data Science, others with department consent. Recommended prep: Basic concepts in machine learning, linear algebra, optimization, statistics.

Grading Basis: Graded

Foundational knowledge in applied aspects of machine learning, including methods for handling uncertain, small, and imbalanced data; feature selection and representation learning; and model selection and assessment. Students will also gain exposure to state-of-the-art research on interpretability of machine learning models, stability of machine learning algorithms, and meta-learning. Topics will be discussed in the context of recent advances in machine learning for materials, chemistry, and physics applications, with an emphasis on the unique opportunities and challenges at the intersection of machine learning and these fields.

Last Refreshed: 04-MAR-24 AM
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Term Class Number Campus Instruction Mode Instructor Section Session Schedule Enrollment Location Credits Grading Basis Notes
Spring 2024 13880 Storrs Online Synchronous Yang, Qian 001 Reg Tu 6:00pm‑7:30pm
8/10 No Room Required - Online 3.00 Graded