Fundamentals of data analytics: with a view to machine learning

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 deriv...

Descripción completa

Detalles Bibliográficos
Autores principales: Mathar, Rudolf, Alirezaei, Gholamreza, Balda, Emilio, Behboodi, Arash
Lenguaje:eng
Publicado: Springer 2020
Materias:
Mathematical Physics and Mathematics
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-56831-3
http://cds.cern.ch/record/2740528
_version_ 1780968334216396800
author Mathar, Rudolf
Alirezaei, Gholamreza
Balda, Emilio
Behboodi, Arash
author_facet Mathar, Rudolf
Alirezaei, Gholamreza
Balda, Emilio
Behboodi, Arash
author_sort Mathar, Rudolf
collection CERN
description 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. .
id cern-2740528
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
publisher Springer
record_format invenio
spelling cern-27405282021-04-21T16:45:48Zdoi:10.1007/978-3-030-56831-3http://cds.cern.ch/record/2740528engMathar, RudolfAlirezaei, GholamrezaBalda, EmilioBehboodi, ArashFundamentals of data analytics: with a view to machine learningMathematical Physics and MathematicsThis 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. .Springeroai:cds.cern.ch:27405282020
spellingShingle Mathematical Physics and Mathematics
Mathar, Rudolf
Alirezaei, Gholamreza
Balda, Emilio
Behboodi, Arash
Fundamentals of data analytics: with a view to machine learning
title Fundamentals of data analytics: with a view to machine learning
title_full Fundamentals of data analytics: with a view to machine learning
title_fullStr Fundamentals of data analytics: with a view to machine learning
title_full_unstemmed Fundamentals of data analytics: with a view to machine learning
title_short Fundamentals of data analytics: with a view to machine learning
title_sort fundamentals of data analytics: with a view to machine learning
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-56831-3
http://cds.cern.ch/record/2740528
work_keys_str_mv AT matharrudolf fundamentalsofdataanalyticswithaviewtomachinelearning
AT alirezaeigholamreza fundamentalsofdataanalyticswithaviewtomachinelearning
AT baldaemilio fundamentalsofdataanalyticswithaviewtomachinelearning
AT behboodiarash fundamentalsofdataanalyticswithaviewtomachinelearning

Ejemplares similares