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" Applied machine learning / "
David Forsyth.
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BL
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| Record Number
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861786
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| Main Entry
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Forsyth, David
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| Title & Author
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Applied machine learning /\ David Forsyth.
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| Publication Statement
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Cham, Switzerland :: Springer,, [2019]
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| Page. NO
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1 online resource (xxi, 494 pages)
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| ISBN
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3030181146
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: 3030181154
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: 3030181162
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: 9783030181147
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: 9783030181154
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: 9783030181161
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3030181138
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9783030181130
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| Bibliographies/Indexes
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Includes bibliographical references and indexes.
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| Contents
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I Classification -- 1 Learning to Classify -- 2 SVMs and random forests -- 3 A little learning theory -- II High dimensional data -- 4 High dimensional data -- 5 Principal component analysis -- 6 Low rank approximations -- 7 Canonical correlation analysis -- III Clustering -- 8 Clustering -- 9 Clustering using probability models -- IV Regression -- 10 Regression -- 11 Regression : choosing and managing models -- 12 Boosting -- V Graphical models -- 13 Hidden Markov models -- 14 Learning sequence models discriminatively -- 15 Mean field inference -- VI Deep networks -- 16 Simple neural networks -- 17 Simple image classifiers -- 18 Classifying images and detecting objects -- 19 Small codes for big signals.
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| Abstract
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Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas. This book is written for people who want to adopt and use the main tools of machine learning, but arent necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. Applied Machine Learning covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing ones own code. A companion to the author's Probability and Statistics for Computer Science, this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use). Emphasizing the usefulness of standard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career. Broad coverage of the area ensures enough to get the reader started, and to realize that its worth knowing more in-depth knowledge of the topic. Practical approach emphasizes using existing tools and packages quickly, with enough pragmatic material on deep networks to get the learner started without needing to study other material.
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| Subject
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Machine learning.
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Machine learning.
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| Dewey Classification
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006.3/1
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| LC Classification
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Q325.5.F67 2019
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