Introduction to Statistical Machine Learning

spring 2017

Discussion session material for STAT 435 (imported from Canvas).

Table of contents

Week 1: Introduction to R and R Markdown

Week 2: Regression

Week 3: Bayes error rate

Week 4: Bootstrap and subset selection

Week 5: Selected exercises (bias-variance tradeoff + QDA)

Week 6: Selected exercises (Curse of dimensionality + Ridge regression)

Week 7: More on penalized regression

Week 8: PCA and nonlinear methods

Week 9: Tree-based methods

Week 10: SVM and review