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
- Co-Author Gareth James' 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
- Lecture Slides
- Lecture Videos Playlist
- Introduction to Classification
- Logistic Regression and Maximum Likelihood
- Multivariate Logistic Regression and Confounding
- Case-Control Sampling and Multi-class Logistic Regression
- Linear Discriminant Analysis and Bayes Theorem
- Univariate Linear Discriminant Analysis
- Multivariate Linear Discriminant Analysis and ROC Curves
- Quadratic Discriminant Analysis and Naive Bayes
- Lab: Logistic Regression
- Lab: Linear Discriminant Analysis
- Lab: K-Nearest Neighbors
- Lecture Slides
- Lecture Videos Playlist
- Linear Model Selection and Best Subset Selection
- Forward Stepwise Selection
- Backward Stepwise Selection
- Estimating Test Error Using Mallow’s Cp, AIC, BIC, Adjusted R-squared
- Estimating Test Error Using Cross-Validation
- Shrinkage Methods and Ridge Regression
- The Lasso
- Tuning Parameter Selection for Ridge Regression and Lasso
- Dimension Reduction
- Principal Components Regression and Partial Least Squares
- Lab: Best Subset Selection
- Lab: Forward Stepwise Selection and Model Selection Using Validation Set
- Lab: Model Selection Using Cross-Validation
- Lab: Ridge Regression and Lasso
- Lecture Slides
- Lecture Videos Playlist
- Unsupervised Learning and Principal Components Analysis
- Exploring Principal Components Analysis and Proportion of Variance Explained
- K-means Clustering
- Hierarchical Clustering
- Breast Cancer Example of Hierarchical Clustering
- Lab: Principal Components Analysis
- Lab: K-means Clustering
- Lab: Hierarchical Clustering