Understanding Machine Learning: From Theory to Algorithms

$64.99 $59.56

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

Quantity

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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Author

Shai Shalev-Shwartz, Shai Ben-David

Binding

Hardcover

EAN

9781107057135

EANList

9781107057135

ISBN

1107057132

IsEligibleForTradeIn

1

ItemDimensions

110, hundredths-inches, 1010, hundredths-inches, 195, hundredths-pounds, 690, hundredths-inches

Label

Cambridge University Press

Languages

English, Unknown, English, Original Language, English, Published

Manufacturer

Cambridge University Press

NumberOfItems

1

NumberOfPages

424

ProductGroup

Book

PublicationDate

2014-05-19

Publisher

Cambridge University Press

Studio

Cambridge University Press

TradeInValue

1814, USD, $18.14