Explores atomic descriptors, emphasizing symmetry, locality, and the challenges of incorporating electrostatics in machine learning models for chemistry.
Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.
Explores applying machine learning to atomic scale systems, emphasizing symmetry in feature mapping and the construction of rotationally invariant descriptors.
