Code and assignment solutions for DATA 558 Spring 22 taught by Professor Daniel Witten
Topics covered:
Bias-variance trade-off; training versus test error; overfitting; cross-validation; subset selection methods; regularized approaches for linear/logistic regression: ridge and lasso; non-parametric regression: trees, bagging, random forests; local regression and splines; generalized additive models; support vector machines; k-means and hierarchical clustering; principal components analysis.
Textbook:
https://www.statlearning.com/