下载链接：http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/copy.html
Understanding Machine Learning: From Theory to Algorithms
By Shai Shalev-Shwartz and Shai Ben-David
Cambridge University Press
About
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.
Table of Contents
Part I: Foundations
- 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
Part 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
Part III: Additional Learning Models
- Online learning
- Clustering
- Dimensionality reduction
- Generative models
- Feature selection and generation
Part IV: Advanced Theory
- Rademacher complexities
- Covering numbers
- Proof of the fundamental theorem of learning theory
- Multiclass learnability
- Compression bounds
- PAC-Bayes
Appendices
- Technical lemmas
- Measure concentration
- Linear algebra