Understanding Machine Learning: From Theory to Algorithms

3.5
ISBN-13: 9781107057135
ISBN-10: 1107057132
Edition: 1
Author: Shai Shalev-Shwartz, Shai Ben-David
Publication date: 2014
Publisher: Cambridge University Press
Format: Hardcover 410 pages
Category: Computers
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Book details

ISBN-13: 9781107057135
ISBN-10: 1107057132
Edition: 1
Author: Shai Shalev-Shwartz, Shai Ben-David
Publication date: 2014
Publisher: Cambridge University Press
Format: Hardcover 410 pages
Category: Computers

Summary

Acknowledged authors Shai Shalev-Shwartz , Shai Ben-David wrote Understanding Machine Learning: From Theory to Algorithms comprising 410 pages back in 2014. Textbook and eTextbook are published under ISBN 1107057132 and 9781107057135. Since then Understanding Machine Learning: From Theory to Algorithms textbook received total rating of 3.5 stars and was available to sell back to BooksRun online for the top buyback price of $ 20.60 or rent at the marketplace.

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.

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