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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

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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.

Reviews

This elegant ebook covers both rigorous theory and practical methods of machine learning. This makes it a rather unique resource, ideal for all those who want to understand how to find structure in data.” – Professor Bernhard Schölkopf, Max Planck Institute for Intelligent Systems

This textbook gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by 2 key contributors to the theoretical foundations in this area, it covers the range from algorithms to theoretical foundations, at a level appropriate for an advanced undergraduate course.” – Dr. Peter L. Bartlett, University of California, Berkeley

This is a timely textbook on the mathematical foundations of machine learning, providing a treatment that is both broad and deep, not only rigorous but also with insight and intuition. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great ebook for anyone interested in the computational and mathematical underpinnings of this important and fascinating field.” – Avrim Blum, Carnegie Mellon University

 

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