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Machine Learning: An Algorithmic Perspective, Second Edition

Machine Learning: An Algorithmic Perspective, Second Edition

Machine Learning: An Algorithmic Perspective, Second Edition
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Machine Learning: An Algorithmic Perspective, Second Edition Hardback - 2014

by Marsland, Stephen

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CRC Press, 2014. Hardcover. Very Good. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.Dust jacket quality is not guaranteed.
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Details

  • Title Machine Learning: An Algorithmic Perspective, Second Edition
  • Author Marsland, Stephen
  • Binding Hardback
  • Edition [ Edition: secon
  • Condition Used - Very good
  • Pages 458
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2014
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index
  • Bookseller's Inventory # G1466583282I4N00
  • ISBN 9781466583283 / 1466583282
  • Weight 2.24 lbs (1.02 kg)
  • Dimensions 10 x 6.9 x 1 in (25.40 x 17.53 x 2.54 cm)
  • Themes
    • Aspects (Academic): Science/Technology Aspects
  • Category Computers - Data Base Management
  • Library of Congress subjects Algorithms, Machine learning
  • Library of Congress Catalogue Number 2014434325
  • Dewey Decimal Code 006.31
  • Quantity available 1

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From the publisher

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.

About the author

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

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