ENMA489L

Selected Topics in Engineering Materials; Machine Learning for Materials Science

The goal of the course is to familiarize the students with basic as well as state of the art knowledge of machine learning and its applications to materials science and engineering. We will cover 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.

Sister Courses: ENMA489A, ENMA489B, ENMA489C, ENMA489D, ENMA489H, ENMA489I, ENMA489M, ENMA489O, ENMA489Q, ENMA489T, ENMA489U, ENMA489W

Past Semesters

3 reviews
Average rating: 4.00

0 reviews
Average rating: N/A

* "W"s are considered to be 0.0 quality points. "Other" grades are not factored into GPA calculation. Grade data not guaranteed to be correct.