Machine Learning

An Algorithmic Perspective, Second Edition

Stephen Marsland (Massey University, Palmerston North, New Zealand)

$158

Hardback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

CRC Press Inc
08 October 2014

Machine learning

Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

Two new chapters on deep belief networks and Gaussian processes

Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material, including a simple implementation for experiments New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman and particle filters Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author’s website.

By: Stephen Marsland (Massey University Palmerston North New Zealand)
Imprint: CRC Press Inc
Country of Publication: United States
Edition: 2nd edition
Dimensions: Height: 254mm, Width: 178mm, Spine: 25mm
Weight: 1.080kg
ISBN: 9781466583283
ISBN 10: 1466583282
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Pages: 458
Publication Date: 08 October 2014
Audience: College/higher education , A / AS level
Format: Hardback
Publisher's Status: Active

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. Optimization and Search. Evolutionary Learning. Reinforcement Learning. Markov Chain Monte Carlo (MCMC) Methods. Graphical Models. Python.

Stephen Marsland is a professor of scientific computing and the postgraduate director of the School of Engineering and Advanced Technology (SEAT) at Massey University. His research interests in mathematical computing include shape spaces, Euler equations, machine learning, and algorithms. He received a PhD from Manchester University

Reviews for Machine Learning: An Algorithmic Perspective, Second Edition

I have been using this textbook for an undergraduate machine learning class for several years. Some of the best features of this book are the inclusion of Python code in the text (not just on a website), explanation of what the code does, and, in some cases, partial numerical run-throughs of the code. This helps students understand the algorithms better than high-level descriptions and equations alone and eliminates many sources of ambiguity and misunderstanding. -Daniel Kifer, Pennsylvania State University Praise for the First Edition: ... 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 ... -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