Machine Learning : The Art and Science of Algorithms that Make Sense of Data - Peter Flach

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Machine Learning

The Art and Science of Algorithms that Make Sense of Data

By: Peter Flach

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Paperback | 20 September 2012

At a Glance

Paperback
416 Pages

Dimensions(cm)
24.61 x 18.9 x 2.16

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As one of the most comprehensive machine learning texts around, this book does justice to the field''s incredible richness, but without losing sight of the unifying principles. Peter Flach''s clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

Industry Reviews

"This textbook is clearly written and well organized. Starting from the basics, the author skillfully guides the reader through his learning process by providing useful facts and insight into the behavior of several machine learning techniques, as well as the high-level pseudocode of many key algorithms." < /br>Fernando Berzal, Computing Reviews

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Machine Learning Computer Programming & Software Development Algorithms & Data Structures Computer Vision Engineering & Technology Electronics & Communications Engineering Pattern Recognition Digital Signal Processing (DSP)

Computer Networking & Communications

Prologue: a machine learning sampler
The ingredients of machine learning
Binary classification and related tasks
Beyond binary classification
Concept learning
Tree models
Rule models
Linear models
Distance-based models
Probabilistic models
Features
In brief: model ensembles
In brief: machine learning experiments
Epilogue: where to go from here
Important points to remember
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

Machine Learning

The Art and Science of Algorithms that Make Sense of Data

At a Glance

Paperback

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

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