Understanding Machine Learning (9781107057135)
Hardcover 410 pages English May 19, 2014
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Cambridge University Press
Understanding Machine Learning (9781107057135)
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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
- Publisher:
- Cambridge University Press
- Publication Date:
- May 19, 2014
- Number of pages:
- 410 pages
- Language:
- English
- Binding:
- Hardcover
- ISBN-10:
- 1107057132
- ISBN-13:
- 9781107057135