Understanding Machine Learning: From Theory to Algorithms

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Understanding Machine Learning: From Theory to Algorithms

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.

Country	USA
Binding	Kindle Edition
Edition	1
EISBN	9781139948517
Format	Kindle eBook
Label	Cambridge University Press
Manufacturer	Cambridge University Press
NumberOfPages	415
PublicationDate	2014-05-31
Publisher	Cambridge University Press
ReleaseDate	2014-05-14
Studio	Cambridge University Press

Understanding Machine Learning: From Theory to Algorithms

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