﻿ ISLR Textbook Slides, Videos and Resources

# Introduction to Statistical Learning:With Applications in R

## Lecture Slides and Videos

### 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)
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 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)