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Understanding machine learning : from theory to algorithms /

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Bibliographic Details
Main Author: Shalev-Shwartz, Shai
Corporate Author: Cambridge University Press
Other Authors: Ben-David, Shai
Format: eBook
Language:English
Published: New York, NY, USA : Cambridge University Press, 2014.
Subjects:
Machine learning.
Algorithms.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction
  • I. 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
  • II. 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
  • III. Additional Learning Models
  • Online learning
  • Clustering
  • Dimensionality reduction
  • Generative models
  • Feature selection and generation
  • IV. Advanced Theory
  • Rademacher complexities
  • Covering numbers
  • Proof of the fundamental theorem of learning theory
  • Multiclass learnability
  • Compression bounds
  • PAC-Bayes.