Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.
Explores model checking and residuals in regression analysis, emphasizing the importance of diagnostics for ensuring model validity.
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Explores the importance of causality for robust machine learning, covering ideal datasets, missing data problems, graphical models, and interference models.
