Understanding Machine Learning:From Theory to Algorithms
Authors:Shai Shalev-Shwartz - Shai Ben-David
Release Finelybook 出版日期：2014-05-19
pages 页数：410 pages
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.
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