ISLR
An Introduction to Statistical Learning
Regression, classification, resampling, trees, SVMs and unsupervised learning — with exercises.
44 notes
Chapter 1 — Introduction
Chapter 2 — Statistical Learning
- What Is Statistical Learning? ISLR · Chapter 2 — Statistical Learning ISLR
- Assessing Model Accuracy ISLR · Chapter 2 — Statistical Learning ISLR
- Statistical Learning: Conceptual Exercises ISLR · Chapter 2 — Statistical Learning ISLR
- Statistical Learning: Applied Exercises ISLR · Chapter 2 — Statistical Learning ISLR
Chapter 3 — Linear Regression
- Simple Linear Regression ISLR · Chapter 3 — Linear Regression ISLR
- Multiple Linear Regression ISLR · Chapter 3 — Linear Regression ISLR
- Other Considerations in the Regression Model ISLR · Chapter 3 — Linear Regression ISLR
- Linear Regression: Conceptual Exercises ISLR · Chapter 3 — Linear Regression ISLR
- Linear Regression: Applied Exercises ISLR · Chapter 3 — Linear Regression ISLR
Chapter 4 — Classification
- Logistic Regression ISLR · Chapter 4 — Classification ISLR
- Linear Discriminant Analysis ISLR · Chapter 4 — Classification ISLR
- Classification: Conceptual Exercises ISLR · Chapter 4 — Classification ISLR
- Classification: Applied Exercises ISLR · Chapter 4 — Classification ISLR
Chapter 5 — Resampling Methods
- Cross-Validation ISLR · Chapter 5 — Resampling Methods ISLR
- The Bootstrap ISLR · Chapter 5 — Resampling Methods ISLR
- Resampling Methods: Conceptual Exercises ISLR · Chapter 5 — Resampling Methods ISLR
- Resampling Methods: Applied Exercises ISLR · Chapter 5 — Resampling Methods ISLR
Chapter 6 — Linear Model Selection and Regularization
- Subset Selection ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
- Shrinkage Methods ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
- Dimension Reduction Methods ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
- Linear Model Selection and Regularization: Conceptual Exercises ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
- Linear Model Selection and Regularization: Applied Exercises ISLR · Chapter 6 — Linear Model Selection and Regularization ISLR
Chapter 7 — Moving Beyond Linearity
- Polynomial Regression, Step Functions, Basis Functions ISLR · Chapter 7 — Moving Beyond Linearity ISLR
- Regression Splines ISLR · Chapter 7 — Moving Beyond Linearity ISLR
- Smoothing Splines ISLR · Chapter 7 — Moving Beyond Linearity ISLR
- Local Regression, Generalized Additive Models ISLR · Chapter 7 — Moving Beyond Linearity ISLR
- Moving Beyond Linearity: Conceptual Exercises ISLR · Chapter 7 — Moving Beyond Linearity ISLR
- Moving Beyond Linearity: Applied Exercises ISLR · Chapter 7 — Moving Beyond Linearity ISLR
Chapter 8 — Tree-Based Methods
- Decision Trees ISLR · Chapter 8 — Tree-Based Methods ISLR
- Bagging, Random Forests, Boosting ISLR · Chapter 8 — Tree-Based Methods ISLR
- Tree-Based Methods: Conceptual Exercises ISLR · Chapter 8 — Tree-Based Methods ISLR
- Tree-Based Methods: Applied Exercises ISLR · Chapter 8 — Tree-Based Methods ISLR
Chapter 9 — Support Vector Machines
- Maximal Margin Classifier ISLR · Chapter 9 — Support Vector Machines ISLR
- Support Vector Classifiers ISLR · Chapter 9 — Support Vector Machines ISLR
- Support Vector Machines and Kernels ISLR · Chapter 9 — Support Vector Machines ISLR
- Support Vector Machines: Conceptual Exercises ISLR · Chapter 9 — Support Vector Machines ISLR
- Support Vector Machines: Applied Exercises ISLR · Chapter 9 — Support Vector Machines ISLR
Chapter 10 — Unsupervised Learning
- Principal Components Analysis ISLR · Chapter 10 — Unsupervised Learning ISLR
- Principal Components Analysis: More on PCA ISLR · Chapter 10 — Unsupervised Learning ISLR
- K-Means Clustering ISLR · Chapter 10 — Unsupervised Learning ISLR
- Hierarchical Clustering ISLR · Chapter 10 — Unsupervised Learning ISLR
- Unsupervised Learning: Conceptual Exercises ISLR · Chapter 10 — Unsupervised Learning ISLR
- Unsupervised Learning: Applied Exercises ISLR · Chapter 10 — Unsupervised Learning ISLR