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.
