Understanding Machine Learning: From Theory to Algorithms (Hardback)
  • Understanding Machine Learning: From Theory to Algorithms (Hardback)
zoom

Understanding Machine Learning: From Theory to Algorithms (Hardback)

(author), (author)
£43.99
Hardback 410 Pages / Published: 19/05/2014
  • We can order this

Usually dispatched within 2 weeks

  • This item has been added to your basket
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: 910 g
Dimensions: 260 x 183 x 28 mm


MEDIA REVIEWS
'This elegant book 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.' Bernhard Schoelkopf, Max Planck Institute for Intelligent Systems, Germany
'This is a timely text on the mathematical foundations of machine learning, providing a treatment that is both deep and broad, not only rigorous but also with intuition and insight. It presents a wide range of classic, fundamental algorithmic and analysis techniques as well as cutting-edge research directions. This is a great book for anyone interested in the mathematical and computational underpinnings of this important and fascinating field.' Avrim Blum, Carnegie Mellon University
'This text gives a clear and broadly accessible view of the most important ideas in the area of full information decision problems. Written by two key contributors to the theoretical foundations in this area, it covers the range from theoretical foundations to algorithms, at a level appropriate for an advanced undergraduate course.' Peter L. Bartlett, University of California, Berkeley

You may also be interested in...

Bayesian Reasoning and Machine Learning
Added to basket
Superintelligence
Added to basket
£18.99
Hardback
Web Data Mining
Added to basket
£54.99
Hardback
Machine Learning
Added to basket
£39.99
Paperback
Probabilistic Graphical Models
Added to basket
Machine Learning
Added to basket
Machine Learning
Added to basket
£52.99
Mixed media product
Machine Learning
Added to basket
£42.50
Paperback
Reinforcement Learning
Added to basket
The Elements of Statistical Learning
Added to basket
Machine Learning for Hackers
Added to basket
Introduction to Machine Learning
Added to basket
Thoughtful Machine Learning
Added to basket
Understanding Machine Learning
Added to basket

Reviews

Please sign in to write a review

Your review has been submitted successfully.