Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks.

Table of Contents

• Introduction
• A gentle start
• A formal learning model
• Learning via uniform convergence
• The bias-complexity trade-off
• The VC-dimension
• Non-uniform learnability
• The runtime of learning
• 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
• Online learning
• Clustering
• Dimensionality reduction
• Generative models
• Feature selection and generation
• Rademacher complexities
• Covering numbers
• Proof of the fundamental theorem of learning theory
• Multiclass learnability
• Compression bounds
• PAC-Bayes

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