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Synopsis
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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About the Authors
Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel.
Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada.
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Understanding Machine Learning: From Theory To Algorithms
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Cambridge University Press, 2014
ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning: From Theory To Algorithms
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ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning
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ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning
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ISBN 13: 9781107057135
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Understanding Machine Learning
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ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning: From Theory to Algorithms
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Cambridge University Press, 2014
ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning
Published by
Cambridge University Press (edition 1), 2014
ISBN 10: 1107057132
ISBN 13: 9781107057135
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Seller: BooksRun, Philadelphia, PA, U.S.A.
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Hardcover. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Seller Inventory # 1107057132-8-1
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Understanding Machine Learning
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ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning
Published by
Cambridge University Press, 2014
ISBN 10: 1107057132
ISBN 13: 9781107057135
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Understanding Machine Learning: From Theory to Algorithms
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Cambridge University Press, United Kingdom, Cambridge, 2014
ISBN 10: 1107057132
ISBN 13: 9781107057135
Used
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Seller: WorldofBooks, Goring-By-Sea, WS, United Kingdom
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Paperback. Condition: Very Good. 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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Seller Inventory # GOR009137965
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