Explores Singular Value Decomposition and Principal Component Analysis for dimensionality reduction, with applications in visualization and efficiency.
Explores atomic descriptors, emphasizing symmetry, locality, and the challenges of incorporating electrostatics in machine learning models for chemistry.
Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.
