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 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. 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 an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook Understanding Machine Learning for free.
- Title
- Understanding Machine Learning
- Subtitle
- From Theory to Algorithms
- Publisher
- Cambridge University Press
- Author(s)
- Shai Ben-David, Shai Shalev-Shwartz
- Published
- 2014-07-17
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 410
- Language
- English
- ISBN-10
- 1107057132
- ISBN-13
- 9781107057135
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
Introduction 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
