Understanding Machine Learning

Understanding Machine Learning: From Theory to Algorithms

Non-Fiction, Computing & Technology, Applications & Programming, Education
Hardback Published on: 19/05/2014
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Synopsis

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.

  • Publisher: Cambridge University Press
  • ISBN: 9781107057135
  • Number of pages: 410
  • Weight: 910g
  • Dimensions: 260 x 183 x 28 mm

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