Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.
Covers conditional distributions and correlations in multivariate statistics, including partial variance and covariance, with applications to non-normal distributions.
Covers local averaging predictors, including K-nearest neighbors and Nadaraya-Watson estimators, as well as local linear regression and its applications.