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portada Machine Learning: An Algorithmic Perspective, Second Edition
Type
Physical Book
Author
Publisher
Year
2014
Language
English
Pages
458
Format
Hardcover
Dimensions
25.4 x 17.5 x 2.5 cm
Weight
1.02 kg.
ISBN13
9781466583283
Edition No.
0002
Categories

Machine Learning: An Algorithmic Perspective, Second Edition

Stephen Marsland (Author) · CRC Press · Hardcover

Machine Learning: An Algorithmic Perspective, Second Edition - Marsland, Stephen

New Book Imported to South Africa
Delivery: 03 Jul - 27 Jul Shipping: 3 to 3 business days.
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R 2,164

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Synopsis "Machine Learning: An Algorithmic Perspective, Second Edition"

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

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