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Machine Learning: An Algorithmic Perspective, Second Edition
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Stephen Marsland (Massey University, Palmerston North, New Zealand)
Machine Learning: An Algorithmic Perspective, Second Edition by Stephen Marsland (Massey University, Palmerston North, New Zealand) at Abbey's Bookshop,

Machine Learning: An Algorithmic Perspective, Second Edition

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


9781466583283

CRC Press Inc


Machine learning


Mixed media product

457 pages

$120.00
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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 New edition
Dimensions:   Height: 254mm,  Width: 178mm,  Spine: 25mm
Weight:   998g
ISBN:   9781466583283
ISBN 10:   1466583282
Pages:   457
Publication Date:   October 2014
Audience:   College/higher education ,  A / AS level
Format:   Mixed media product
Publisher's Status:   Active

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


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

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