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Understanding Machine Learning

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 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.

Shai Ben-David is a Professor in the School of Computer Science at the

University of Waterloo, Canada.