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Machine Learning

Machine Learning

An Algorithmic Perspective, Second Edition

by Stephen Marsland
Hardback
Publication Date: 17/11/2014
RRP  $145.00 $133.90
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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.
ISBN:
9781466583283
9781466583283
Category:
Machine learning
Format:
Hardback
Publication Date:
17-11-2014
Language:
English
Publisher:
Taylor & Francis Inc
Country of origin:
United States
Edition:
2nd Edition
Pages:
457
Dimensions (mm):
254x178x25mm
Weight:
1kg

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