Understanding Machine Learning From Theory to Algorithms by Shalev-Shwartz and Ben-David

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Understanding Machine Learning From Theory to Algorithms

PDF Free Download | Understanding Machine Learning From Theory to Algorithms by Shalev-Shwartz and Ben-David

Contents of Understanding Machine Learning eBook

  • Introduction
  • Part 1 Foundations
  • A Gentle Start
  • A Formal Learning Model
  • Learning via Uniform Convergence
  • The Bias-Complexity Tradeoff
  • The VC-Dimension
  • Nonuniform Learnability
  • The Runtime of Learning
  • Part 2 From Theory to Algorithms
  • Linear Predictors
  • Boosting
  • Model Selection and Validation
  • Convex Learning Problems
  • Regularization and Stability
  • Stochastic Gradient Descent
  • Support Vector Machines
  • Kernel Methods
  • Multiclass, Ranking, and Complex Prediction Problems
  • Decision Trees
  • Nearest Neighbor
  • Neural Networks
  • Part 3 Additional Learning Models
  • Online Learning
  • Clustering
  • Dimensionality Reduction
  • Generative Models
  • Feature Selection and Generation
  • Part 4 Advanced Theory
  • Rademacher Complexities
  • Covering Numbers
  • Proof of the Fundamental Theorem of Learning Theory
  • Multiclass Learnability
  • Compression Bounds
  • PAC-Bayes

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