printer friendly pageCHEM 263: Machine Learning for Chemical and Dynamical Data
Introduction to machine learning methodologies for the chemical sciences, with an emphasis on the current state-of-the-art for applications to both experimental and computational data. The course will be hands-on and final projects will be a major component of the coursework. Material covered will include neural networks, classification and regression, image analysis, graph neural networks, learning potential energy surfaces, coarse-graining, Monte Carlo simulation, and applications to quantum chemistry and molecular dynamics. Prerequisite: knowledge of undergraduate level quantum mechanics and statistical mechanics at the levels of
Chem 173 and
Chem 175. Experience with Python highly recommended.
Terms: Aut
| Units: 3
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