Introduction to Statistical Learning:
With Applications in R

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani

Lecture Slides and Videos

 

Textbook

Amazon: http://www.amazon.com/dp/1461471370
Springer: http://www.springer.com/statistics/statistical+theory+and+methods/book/978-1-4614-7137-0
Author's website: http://www-bcf.usc.edu/~gareth/ISL/
Free textbook PDF:  http://www-bcf.usc.edu/~gareth/ISL/ISLR%20First%20Printing.pdf
Data sets: http://www-bcf.usc.edu/~gareth/ISL/data.html
R code: http://www-bcf.usc.edu/~gareth/ISL/code.html

Lectures by the Authors 

Ch 1: Introduction (slides)

  1. Opening Remarks (18:18)
  2. Machine and Statistical Learning (12:12)

Ch 2: Statistical Learning (slides)

  1. Statistical Learning and Regression (11:41)
  2. Parametric vs. Non-Parametric Models (11:40)
  3. Model Accuracy (10:04)
  4. K-Nearest Neighbors (15:37)
  5. Lab: Introduction to R (14:12)

Ch 3: Linear Regression (slides)

  1. Simple Linear Regression (13:01)
  2. Hypothesis Testing (8:24)
  3. Multiple Linear Regression (15:38)
  4. Model Selection (14:51)
  5. Interactions and Non-Linear Models (14:16)
  6. Lab: Linear Regression (22:10)

Ch 4: Classification (slides)

  1. Introduction (10:25)
  2. Logistic Regression (9:07)
  3. Multivariate Logistic Regression (9:53)
  4. Multiclass Logistic Regression (7:28)
  5. Linear Discriminant Analysis (7:12)
  6. Univariate Linear Discriminant Analysis (7:37)
  7. Multivariate Linear Discriminant Analysis (17:42)
  8. Quadratic Discriminant Analysis (10:07)
  9. Lab: Logistic Regression (10:14)
  10. Lab: Linear Discriminant Analysis (8:22)
  11. Lab: K-Nearest Neighbors (5:01)

Ch 5: Cross Validation (slides)

  1. Prediction Error and Validation Set (14:01)
  2. K-Fold Cross-Validation (13:33)
  3. Cross-Validation Do's and Don'ts (10:07)
  4. Bootstrap 1 (11:29)
  5. Bootstrap 2 (14:35)
  6. Lab: Cross-Validation (11:21)
  7. Lab: Bootstrap (7:40)

Ch 6: Variable Selection (slides)

  1. Linear Model Subset Selection (13:44)
  2. Forward Stepwise Selection (12:26)
  3. Backward Stepwise Selection (5:26)
  4. Estimating Test Error -- Mallow's Cp, AIC, BIC, Adjusted R-squared (14:06)
  5. Estimating Test Error -- Cross-Validation (8:43)
  6. Ridge Regression (12:37)
  7. Lasso (15:21)
  8. Tuning Parameters (5:27)
  9. Dimension Reduction (4:45)
  10. Principal Components and Partial Least Squares (15:48)
  11. Lab: Best Subset Selection (10:36)
  12. Lab: Model Selection -- Forward Stepwise and Validation Set (10:32)
  13. Lab: Model Selection -- Cross-Validation (5:32)
  14. Lab: Ridge Regression and Lasso (16:34)

Ch 7: Non-Linear Models (slides)

  1. Polynomial Regression (14:59)
  2. Piecewise Regression and Splines (13:13)
  3. Smoothing Splines (10:10)
  4. Local Regression and Generalized Additive Models (10:45)
  5. Lab: Polynomials (21:11)
  6. Lab: Splines and Generalized Additive Models (12:15)

Ch 8: Decision Trees (slides)

  1. Decision Trees (14:37)
  2. Pruning Trees (11:45)
  3. Classification Trees (11:00)
  4. Bootstrap Aggregation (Bagging) and Random Forests (13:45)
  5. Boosting (12:03)
  6. Lab: Decision Trees (10:13)
  7. Lab: Random Forests and Boosting (15:35)

Ch 9: Support Vector Machines (slides)

  1. Maximal Margin Classifier (11:35)
  2. Support Vector Classifier (8:04)
  3. Kernels and Support Vector Machines (15:04)
  4. Comparison with Logistic Regression (14:47)
  5. Lab: Support Vector Machine (10:13)
  6. Lab: Nonlinear Support Vector Machine (7:54)

Ch 10: Principal Components and Clustering (slides)

  1. Principal Components Analysis (12:37)
  2. Proportion of Variance Explained (17:39)
  3. K-Means Clustering (17:17)
  4. Hierarchical Clustering (14:45)
  5. Example of Hierarchical Clustering (9:24)
  6. Lab: Principal Components Analysis (6:28)
  7. Lab: K-Means Clustering (6:31)
  8. Lab: Hierarchical Clustering (6:33)

Interviews

  1. John Chambers (10:20)
  2. Bradley Efron (12:08)
  3. Jerome Friedman (10:29)
  4. Statistics Graduate Students (7:44)