Machine Learning
An Algorithmic Perspective
Published on 23. March 2011
406 pages
978-1-4200-6719-4 (ISBN)
Description
Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but
Reviews / Votes
... liberally illustrated with many programming examples, using Python. It includes a basic primer on Python and has an accompanying website.It has excellent breadth, and is comprehensive in terms of the topics it covers, both in terms of methods and in terms of concepts and theory. ...
I think the author has succeeded in his aim: the book provides an accessible introduction to machine learning. It would be excellent as a first exposure to the subject, and would put the various ideas in context ...
This book also includes the first occurrence I have seen in print of a reference to a zettabyte of data (1021 bytes) - a reference to "all the world's computers" being estimated to contain almost a zettabyte by 2010.
-David J. Hand, International Statistical Review (2010), 78
If you are interested in learning enough AI to understand the sort of new techniques being introduced into Web 2 applications, then this is a good place to start. ... it covers the subject matter of many an introductory course on AI and it has references to the source material and further reading but it is written in a fairly casual style. Overall it works and much of the mathematics is explained in ways that make it fairly clear what is going on ... . This is a suitable introduction to AI if you are studying the subject on your own and it would make a good course text for an introduction and overview of AI.
-I-Programmer, November 2009
More details
Series
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Undergraduate and graduate students in machine learning and neural networks courses.
Illustrations
168 s/w Abbildungen
168 b/w images
File size
6,68 MB
ISBN-13
978-1-4200-6719-4 (9781420067194)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Book
04/2009
1st Edition
Chapman & Hall/CRC
€85.60
Article exhausted; check for reprint
Content
Introduction. Linear Discriminants. The Multi-Layer Perceptron. Radial Basis Functions and Splines. Support Vector Machines. Learning with Trees. Decision by Committee: Ensemble Learning. Probability and Learning. Unsupervised Learning. Dimensionality Reduction. Optimisation and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.
