Explores applying machine learning to atomic scale systems, emphasizing symmetry in feature mapping and the construction of rotationally invariant descriptors.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
Explores the application of machine learning in molecular dynamics and materials, emphasizing the creation of meaningful features and the importance of generalizability.
