Contents#

• Introduction

  • CH1: Introduction

• Foundations

  • CH2: Probability: Univariate Models
  • CH3: Probability: Multivariate Models
  • CH4: Statistics
  • CH5: Decision Theory
  • CH6: Information Theory
  • CH7: Linear Algebra]
  • CH8: Optimization

• Linear Models

  • CH9: Linear Discriminant Analysis
  • CH10: Logistic Regression
  • CH11: Linear Regression
  • CH12: Generalized Linear Models

• Deep Neural Networks

  • CH13: Neural Networks for Structured Data
  • CH14: Neural Networks for Images
  • CH15: Neural Networks for Sequences

• Nonparametric Models

  • CH16: Exemplar-based Methods
  • CH17: Kernel Methods
  • CH18: Trees, Forests, Bagging and Boosting

• Beyond Supervised Learning

  • CH19: Learning with Fewer Labeled Examples
  • CH20: Dimensionality Reduction
  • CH21: Clustering
  • CH22: Recommender Systems
  • CH23: Graph Embeddings