Understanding Machine Learning From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms
Authors: Shai Shalev-Shwartz - Shai Ben-David
ISBN-10: 1107057132
ISBN-13: 9781107057135
Edition 版本: 1
Publication Date 出版日期: 2014-05-19
Pages: 410 pages
Book 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.