Stanford - Statistical Learning R Code

Code for the Stanford Statistical Learning course

When I took the Statistical Learning course over at Stanford Lagunita, I also wrote R code to tackle the practical exercises in the accompanying book, An Introduction to Statistical Learning. I uploaded the code to a GitHub repository. Here is a brief description of the code.

Chapter 1 Introduction	no code
Chapter 2 Overview of Statistical Learning	basic introductory R code
Chapter 3 Linear Regression	(multiple) linear regressions
Chapter 4 Classification	logistic regression and discriminant analysis
Chapter 5 Resampling Methods	cross-validation and bootstrap
Chapter 6 Selection and Regularization	stepwise model selection, lasso, ridge regression, etc.
Chapter 7 Moving Beyond Linearity	fitting polynomials, splines, generalized additive models
Chapter 8 Tree-Based Methods	trees, bagging, random forests and boosting
Chapter 9 Support Vector Machines	SVMs in R
Chapter 10 Unsupervised Learning	principal components; k-means and hierarchical clustering