Understanding Machine Learning: From Theory to Algorithms


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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.

Author: Shai Shalev-Shwartz, Shai Ben-David
Publisher: Cambridge University Press
Published: 05/19/2014
Pages: 410
Binding Type: Hardcover
Weight: 1.95lbs
Size: 10.10h x 6.90w x 1.10d
ISBN13: 9781107057135
ISBN10: 1107057132
BISAC Categories:
- Computers | Artificial Intelligence | Computer Vision & Pattern Recognit