Fall 2024 Online Courses
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5835. Machine Learning for Physical Sciences and Systems
3.00 credits
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.
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