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 princi- pled 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. Fol- lowing a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous text- books. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorith- mic 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 stu- dents and nonexpert readers in statistics, computer science, mathematics, and engineering.
Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at The Hebrew University, Israel.
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