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A First Course in Machine Learning

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by Simon Rogers & Mark Girolami
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A First Course in Machine Learning by Simon Rogers & Mark Girolami

A First Course in Machine Learning covers the core mathematical and statistical techniques needed to understand some of the most popular machine learning algorithms. The algorithms presented span the main problem areas within machine learning: classification, clustering and projection. The text gives detailed descriptions and derivations for a small number of algorithms rather than cover many algorithms in less detail. Referenced throughout the text and available on a supporting website (, an extensive collection of MATLAB?/Octave scripts enables students to recreate plots that appear in the book and investigate changing model specifications and parameter values. By experimenting with the various algorithms and concepts, students see how an abstract set of equations can be used to solve real problems. Requiring minimal mathematical prerequisites, the classroom-tested material in this text offers a concise, accessible introduction to machine learning. It provides students with the knowledge and confidence to explore the machine learning literature and research specific methods in more detail

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Ebook Details
Pages: 305
Size: 30.6 MB
Publisher: CRC Press
Date published:   2011
ISBN: 9781466506299 (DRM-PDF)

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This product is listed in the following categories:

Nonfiction > Computers
Nonfiction > Business & Economics > Statistics
Nonfiction > Computers > Database Management > Database Mining

These authors have products in the following categories:

Nonfiction > Science > Life Sciences > Genetics & Genomics
Nonfiction > Reference
Nonfiction > Computers
Nonfiction > Business & Economics > Statistics
Nonfiction > Computers > Database Management > Database Mining
Nonfiction > Computers > Machine Theory

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