ENMA437
Machine Learning for Materials Science
Prerequisite: MATH206, ENMA300, and MATH461. Restriction: Permission of ENGR-Materials Science & Engineering department. Jointly offered with: ENMA637. Credit only granted for: ENMA489L, ENMA437 or ENMA637. Formerly: ENMA489L. Familiarizes students with basic as well as state of the art knowledge of machine learning and its applications to materials science and engineering. Covers the range of machine learning topics with applications including feature identification and extraction, determining predictive descriptors, uncertainty analysis, and identifying the most informative experiment to perform next. One focus of the class is to build the skills necessary for developing an autonomous materials research system, where machine learning controls experiment design, execution, and analysis in a closed-loop.
Fall 2024
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Fall 2023
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Past Semesters
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