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Probabilistic machine learning for civil engineers

Probabilistic machine learning for civil engineers (Loan 1 times)

Material type
단행본
Personal Author
Goulet, James-A.
Title Statement
Probabilistic machine learning for civil engineers / James-A. Goulet.
Publication, Distribution, etc
Cambridge, Massachusetts :   The MIT Press,   2020.  
Physical Medium
xxviii, 269 p. : ill. (some col.) ; 26 cm.
ISBN
9780262538701 (paperback)
요약
"The book introduces probabilistic machine learning concepts to civil engineering students and professionals, who typically do not have the background necessary to understand the subject from a purely computer science perspective. It presents key approaches among the three sub-fields of machine learning: supervised, unsupervised, and reinforcement learning. The methods are demonstrated through step-by-step examples and copius illustrations in order to simplify abstract concepts. The book will prepare readers to access the vast body of literature from the field of machine learning"--
Bibliography, Etc. Note
Includes bibliographical references and index.
Subject Added Entry-Topical Term
Machine learning. Probabilities.
000 00000nam u2200205 a 4500
001 000046037774
005 20200723150324
008 200723s2020 maua b 001 0 eng d
010 ▼a 2019027152
020 ▼a 9780262538701 (paperback)
020 ▼z 9780262358019 (ebook)
035 ▼a (KERIS)REF000019151366
040 ▼a DLC ▼b eng ▼e rda ▼c DLC ▼d 211009
050 0 0 ▼a Q325.5 ▼b .G68 2020
082 0 0 ▼a 006.3/1 ▼2 23
084 ▼a 006.31 ▼2 DDCK
090 ▼a 006.31 ▼b G698p
100 1 ▼a Goulet, James-A.
245 1 0 ▼a Probabilistic machine learning for civil engineers / ▼c James-A. Goulet.
260 ▼a Cambridge, Massachusetts : ▼b The MIT Press, ▼c 2020.
300 ▼a xxviii, 269 p. : ▼b ill. (some col.) ; ▼c 26 cm.
504 ▼a Includes bibliographical references and index.
520 ▼a "The book introduces probabilistic machine learning concepts to civil engineering students and professionals, who typically do not have the background necessary to understand the subject from a purely computer science perspective. It presents key approaches among the three sub-fields of machine learning: supervised, unsupervised, and reinforcement learning. The methods are demonstrated through step-by-step examples and copius illustrations in order to simplify abstract concepts. The book will prepare readers to access the vast body of literature from the field of machine learning"-- ▼c Provided by publisher.
650 0 ▼a Machine learning.
650 0 ▼a Probabilities.
945 ▼a KLPA

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Science & Engineering Library/Sci-Info(Stacks2)/ Call Number 006.31 G698p Accession No. 121253866 Availability In loan Due Date 2021-07-06 Make a Reservation Available for Reserve R Service M

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