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Fundamentals of data analyticswith a...

Mathar, Rudolf.

Fundamentals of data analyticswith a view to machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of data analyticsby Rudolf Mathar ... [et al.].
Reminder of title:	with a view to machine learning /
other author:	Mathar, Rudolf.
Published:	Cham :Springer International Publishing :2020.
Description:	xi, 127 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	StatisticsData processing.
Online resource:	https://doi.org/10.1007/978-3-030-56831-3
ISBN:	9783030568313$q(electronic bk.)

Fundamentals of data analyticswith a view to machine learning /
Fundamentals of data analyticswith a view to machine learning /[electronic resource] :by Rudolf Mathar ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 127 p. :ill. (some col.), digital ;24 cm.

1 Introduction -- 2 Prerequisites from Matrix Analysis -- 3 Multivariate Distributions and Moments -- 4 Dimensionality Reduction -- 5 Classification and Clustering -- 6 Support Vector Machines -- 7 Machine Learning -- Index.

This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.

ISBN: 9783030568313$q(electronic bk.)

Standard No.: 10.1007/978-3-030-56831-3doiSubjects--Topical Terms:

183693
Statistics
--Data processing.

LC Class. No.: QA276.4

Dewey Class. No.: 519.5

Fundamentals of data analyticswith a view to machine learning /
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245 0 0 $a Fundamentals of data analytics $h [electronic resource] : $b with a view to machine learning / $c by Rudolf Mathar ... [et al.].
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300 $a xi, 127 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 1 Introduction -- 2 Prerequisites from Matrix Analysis -- 3 Multivariate Distributions and Moments -- 4 Dimensionality Reduction -- 5 Classification and Clustering -- 6 Support Vector Machines -- 7 Machine Learning -- Index.
520 $a This book introduces the basic methodologies for successful data analytics. Matrix optimization and approximation are explained in detail and extensively applied to dimensionality reduction by principal component analysis and multidimensional scaling. Diffusion maps and spectral clustering are derived as powerful tools. The methodological overlap between data science and machine learning is emphasized by demonstrating how data science is used for classification as well as supervised and unsupervised learning.
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650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
700 1 $a Mathar, Rudolf. $3 877611
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