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.
Prerequisites: Open to graduate students in the Computer Science and Engineering program, others with department consent. Recommended preparation: Familiarity with basic concepts in machine learning, linear algebra, optimization, and statistics (optional supplementary material will be provided for review). A background and interest in applications in the physical sciences is preferable.
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: 09-AUG-22 05.20.17.316132 AM
|Term||Class Number||Campus||Instruction Mode||Instructor||Section||Session||Schedule||Enrollment||Location||Credits||Grading Basis||Notes|
|1223 16134 1 001||Spring 2022||16134||Storrs||Distance Learning||Yang, Qian||001||Reg||Mo 6:00pm‑7:30pm
||11/15||No Room Required - Online||3.00||Graded|
|1228 14265 1 001||Fall 2022||14265||Storrs||Distance Learning||Yang, Qian||001||Reg||Mo 6:00pm‑7:30pm
||15/15||No Room Required - Online||3.00||Graded|