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

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 $ 17.79 or rent at the marketplace.

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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