Understanding Machine Learning : From Theory to Algorithms Hardback
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.
- Format: Hardback
- Pages: 410 pages, Worked examples or Exercises; 1 Halftones, unspecified; 46 Line drawings, unspecified
- Publisher: Cambridge University Press
- Publication Date: 19/05/2014
- Category: Machine learning
- ISBN: 9781107057135