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A computational approach to statisti...

Arnold, Taylor,

A computational approach to statistical learning /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A computational approach to statistical learning /Taylor Arnold, Michael Kane, Bryan W. Lewis.
Author:	Arnold, Taylor,
other author:	Kane, Michael
Published:	Boca Raton, FL :CRC Press, Taylor & Francis Group,c2019.
Description:	xiii, 361 p. :ill. ;25 cm.
Subject:	Machine learningMathematics.
ISBN:	9781138046375 :
ISSN:	cam a2200277 a 450

A computational approach to statistical learning /
Arnold, Taylor,

A computational approach to statistical learning /Taylor Arnold, Michael Kane, Bryan W. Lewis. - Boca Raton, FL :CRC Press, Taylor & Francis Group,c2019. - xiii, 361 p. :ill. ;25 cm. - Chapman & Hall/CRC texts in statistical science series. - Texts in statistical science..

Includes bibliographical references and index.

A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. --

ISBN: 9781138046375 :GBP$65

ISSN: cam a2200277 a 450

LCCN: 2018049270

Nat. Bib. No.: GBB8M6332bnb

Nat. Bib. Agency Control No.: 019157667UkSubjects--Topical Terms:

857106
Machine learning
--Mathematics.

LC Class. No.: Q325.5 / .A76 2019

Dewey Class. No.: 006.3/1015195

A computational approach to statistical learning /
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100 1 $a Arnold, Taylor, $e author. $3 857956
245 1 2 $a A computational approach to statistical learning / $c Taylor Arnold, Michael Kane, Bryan W. Lewis.
260 $a Boca Raton, FL : $b CRC Press, Taylor & Francis Group, $c c2019.
300 $a xiii, 361 p. : $b ill. ; $c 25 cm.
490 1 $a Chapman & Hall/CRC texts in statistical science series
504 $a Includes bibliographical references and index.
520 $a A Computational Approach to Statistical Learning gives a novel introduction to predictive modeling by focusing on the algorithmic and numeric motivations behind popular statistical methods. The text contains annotated code to over 80 original reference functions. These functions provide minimal working implementations of common statistical learning algorithms. Every chapter concludes with a fully worked out application that illustrates predictive modeling tasks using a real-world dataset. The text begins with a detailed analysis of linear models and ordinary least squares. Subsequent chapters explore extensions such as ridge regression, generalized linear models, and additive models. The second half focuses on the use of general-purpose algorithms for convex optimization and their application to tasks in statistical learning. Models covered include the elastic net, dense neural networks, convolutional neural networks (CNNs), and spectral clustering. A unifying theme throughout the text is the use of optimization theory in the description of predictive models, with a particular focus on the singular value decomposition (SVD). Through this theme, the computational approach motivates and clarifies the relationships between various predictive models. -- $c Provided by publisher.
650 0 $a Machine learning $x Mathematics. $3 857106
650 0 $a Mathematical statistics. $3 181877
650 0 $a Estimation theory. $3 181864
700 1 $a Kane, Michael $q (Michael John), $e author. $3 857957
700 1 $a Lewis, Bryan W. $q (Bryan Wayne), $e author. $3 857958
776 0 8 $i Ebook version : $z 9781351694759
830 0 $a Texts in statistical science. $3 205786