1.9 How is the book divided?

The book is divided into 13 chapters covering:

  • Introduction and Statistical Learning:
    • Supervised Versus Unsupervised Learning
    • Regression Versus Classification Problems

Linear statistical learning

  • Linear Regression:
    • basic concepts
    • introduction of K-nearest neighbor classifier
  • Classification:
    • logistic regression
    • linear discriminant analysis
  • Resampling Methods:
    • cross-validation
    • the bootstrap
  • Linear Model Selection and Regularization: potential improvements over standard linear regression
    • stepwise selection
    • ridge regression
    • principal components regression
    • the lasso

Non-linear statistical learning

  • Moving Beyond Linearity:

    • Polynomial Regression
    • Regression Spline
    • Smoothing Splines
    • Local Regression
    • Generalized Additive Models

  • Tree-Based Methods:

    • Decision Trees
    • Bagging, Random Forests, Boosting, and Bayesian Additive Regression Trees

  • Support Vector Machines (linear and non-linear classification)

  • Deep Learning (non-linear regression and classification)

  • Survival Analysis and Censored Data

  • Unsupervised Learning:

    • Principal components analysis
    • K-means clustering
    • Hierarchical clustering

  • Multiple Testing

Each chapter includes 1 self-contained R lab on the topic