From Theory to Algorithms

of the book. In particular, page numbers are not identical (but section numbers are the same). 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 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 Compute