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Understanding Machine Learning : From Theory to Algorithms, Hardback Book

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

  • Format: Hardback
  • Pages: 410 pages, Worked examples or Exercises; 1 Halftones, unspecified; 46 Line drawings, unspecified
  • Publisher: Cambridge University Press
  • Publication Date:
  • Category: Machine learning
  • ISBN: 9781107057135

£44.99

£39.95

 
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