Understanding Machine Learning: From Theory to Algorithms – eBook


eBook details

  • Authors: Shai Shalev-Shwartz, Shai Ben David
  • File Size: 3 MB
  • Format: PDF
  • Length: 415 pages
  • Publisher: Cambridge University Press; 1st edition
  • Publication Date: May 19, 2014
  • Language: English
  • ASIN: B00J8LQU8I
  • ISBN-10: 1107057132, 1107512824
  • ISBN-13: 9781107057135, 9781107512825


Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this digital textbook Understanding Machine Learning: From Theory to Algorithms (PDF) is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The ebook 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 ebook 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 stability and convexity; important algorithmic paradigms including neural networks, stochastic gradient descent, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for beginning graduates or advanced undergraduates, the textbook makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in computer science, mathematics, statistics, and engineering.