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Online Courses – Fall CSE 5835

5835. Machine Learning for Physical Sciences and Systems

3.00 credits

Prerequisites: Open to MS and PhD students in the Computer Science and Engineering program, others with department consent. Recommended preparation: Coursework and/or project experience in machine learning. Background in linear algebra, optimization, and statistics. Interest in applications in the physical sciences is preferable but not required.

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.

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