PSL_Book
  • 1. Introduction
    • 1.1. Introduction to statistical learning
    • 1.2. Least squares vs. nearest neighbors
  • 2. Linear Regression
  • 3. Variable Selection and Regularization
  • 4. Regression Trees and Ensemble
  • 5. Nonlinear Regression
  • 6. Clustering Analysis
  • 7. Latent Structure Models
  • 8. TBA
  • 9. Discriminant Analysis
  • 10. Logistic Regression
  • 11. Support Vector Machine
  • 12. Classification Trees and Boosting
  • 13. Recommender System
PSL_Book
  • 1. Introduction

1. Introduction

  • 1.1. Introduction to statistical learning
    • 1.1.1. Types of statistical learning problems
    • 1.1.2. Challenge of supervised learning
    • 1.1.3. Curse of dimensionality
    • 1.1.4. A Glimpse of Learning Theory
    • 1.1.5. Bias and variance tradeoff
  • 1.2. Least squares vs. nearest neighbors
    • 1.2.1. Introduction to LS and kNN
    • 1.2.2. Simulation Study
    • 1.2.3. Compute Bayes rule
    • 1.2.4. Discussion
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