Contents

Table of Contents

  1. Ordinary Linear Regression

    1. The Loss-Minimization Perspective

    2. The Likelihood-Maximization Perspective

  2. Linear Regression Extensions

    1. Regularized Regression (Ridge and Lasso)

    2. Bayesian Regression

    3. Generalized Linear Models (GLMs)

  3. Discriminative Classification

    1. Logistic Regression

    2. The Perceptron Algorithm

    3. Fisher’s Linear Discriminant

  4. Generative Classification

    (Linear and Quadratic Discriminant Analysis, Naive Bayes)

  5. Decision Trees

    1. Regression Trees

    2. Classification Trees

  6. Tree Ensemble Methods

    1. Bagging

    2. Random Forests

    3. Boosting

  7. Neural Networks

Introduction Conventions and Notation