Chapter 2: Statistical Learning
Chapter 6: Linear Model Selection and Regularization
Chapter 7: Moving Beyond Linearity
Chapter 9: Support Vector Machines
Chapter 10: Unsupervised Learning
- Official ISLR Website
- An Introduction to Statistical Learning with Applications in R - Corrected 6th Printing PDF
- DataSchool.io - In-depth introduction to machine learning in 15 hours of expert videos
- Statistical Learning with R - Stanford Online Lecture Videos Playlist
- The Second Edition of the course also covers deep learning, survival analysis, and multiple testing, which these notes do not cover
- Lecture Slides
- Lecture Videos
- 4.1 Introduction to Classification Problems
- 4.2 Logistic Regression
- 4.3 Multivariate Logistic Regression
- 4.4 Logistic Regression Case-Control Sampling and Multi-class
- 4.5 Discriminant Analysis
- 4.6 Gaussian Discriminant Analysis (One Variable)
- 4.7 Gaussian Discriminant Analysis (Many Variables)
- 4.8 Generalized Linear Models
- 4.9 Quadratic Discriminant Analysis and Naive Bayes
- 4.R.1 Lab: Logistic Regression
- 4.R.2 Lab: Linear Discriminant Analysis
- 4.R.3 Lab: Nearest Neighbor Classification
- Lecture Slides
- Lecture Videos
- 6.1 Introduction and Best Subset Selection
- 6.2 Stepwise Selection
- 6.3 Backward Stepwise Selection
- 6.4 Estimating Test Error
- 6.5 Validation and Cross-Validation
- 6.6 Shrinkage Methods and Ridge Regression
- 6.7 The Lasso
- 6.8 Tuning Parameter Selection
- 6.9 Dimension Reduction Methods
- 6.10 Principal Components Regression and Partial Least Squares
- 6.R.1 Lab: Markdown in RStudio and Best Subset Regression
- 6.R.2 Lab: Forward Stepwise Regression
- 6.R.3 Lab: Model Selection and Cross-Validation
- 6.R.4 Lab: Ridge Regression and Lasso