Understanding Machine Learning : From Theory to Algorithms PDF
by Shai Shalev-Shwartz, Shai Ben-David
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 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.
Information
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Download - Immediately Available
- Format:PDF
- Publisher:Cambridge University Press
- Publication Date:19/05/2014
- Category:
- ISBN:9781139950619
Other Formats
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Information
-
Download - Immediately Available
- Format:PDF
- Publisher:Cambridge University Press
- Publication Date:19/05/2014
- Category:
- ISBN:9781139950619
