num_pages: 410
description: 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.
language: English
title: Understanding Machine Learning: From Theory to Algorithms
publisher: Cambridge University Press