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Information-theoretic methods in data science

PublishedCambridge : Cambridge University Press, 2021
Detail1 online resource (xxi, 538 pages) : digital, PDF file(s)
Connect toFull text E-books on CambridgeCore
SubjectData mining
Information Theory
Machine learning
Electronic books
Science and Technology Branch
E-books--Computer Science
E-books--Engineering
Added AuthorRodrigues, Miguel R. D., Ed
Eldar, Yonina C., Ed
ISBN9781108616799 (e-book)
General NotePurchased by Central Library
SummaryLearn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics
Restrictions on Access NoteAccess is only allowed for Authorized Users within the KKU internet network or can be accessed externally through a KKU VPN Internet@Home
ประเภทแหล่งที่มา Computer Files


TagData
TitleInformation-theoretic methods in data science [electronic resources] / edited by Miguel R. D. Rodrigues, Yonina C. Eldar
PublishedCambridge : Cambridge University Press, 2021
Detail1 online resource (xxi, 538 pages) : digital, PDF file(s)
NotePurchased by Central Library
AbstractLearn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics
Subject
Subject
Subject
Subject
Subject
Subject
Subject
Added AuthorRodrigues, Miguel R. D., Ed
Added AuthorEldar, Yonina C., Ed
ISBN9781108616799 (e-book)
999วราภรณ์/e
999QA76.9.D343 I64 2021
Restrictions on Access NoteAccess is only allowed for Authorized Users within the KKU internet network or can be accessed externally through a KKU VPN Internet@Home
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24500|aInformation-theoretic methods in data science|h[electronic resources] /|cedited by Miguel R. D. Rodrigues, Yonina C. Eldar.
260##|aCambridge :|bCambridge University Press,|c2021.
300##|a1 online resource (xxi, 538 pages) :|bdigital, PDF file(s).
500##|aPurchased by Central Library.
5061#|aAccess is only allowed for Authorized Users within the KKU internet network or can be accessed externally through a KKU VPN Internet@Home
520##|aLearn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.
650#0|aData mining.
650#0|aInformation Theory.
650#0|aMachine learning.
650#0|aElectronic books.
655#4|aScience and Technology Branch.
655#4|aE-books--Computer Science.
655#4|aE-books--Engineering.
7001#|aRodrigues, Miguel R. D.,|eEd.
7001#|aEldar, Yonina C.,|eEd.
85640|uhttps://doi.org/10.1017/9781108616799|zFull text E-books on CambridgeCore
TagData
TitleInformation-theoretic methods in data science
SubjectData mining.
SubjectElectronic books.
SubjectInformation Theory.
SubjectMachine learning.
SubjectE-books--Computer Science.
SubjectE-books--Engineering.
SubjectScience and Technology Branch.
DescriptionPurchased by Central Library.
DescriptionLearn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine learning, and statistics.
PublisherCambridge : Cambridge University Press,
ContributorEldar, Yonina C.,
ContributorRodrigues, Miguel R. D.,
Date2021
Date2021.
Typeno type provided
TypeE-books--Computer Science.
TypeE-books--Engineering.
TypeScience and Technology Branch.
Identifier9781108616799
Identifierhttps://doi.org/10.1017/9781108616799
Languageeng
RightsAccess is only allowed for Authorized Users within the KKU internet network or can be accessed externally through a KKU VPN Internet@Home

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