Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.
Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.
Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.
Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.
