Probabilistic machine learning for civil engineers / James-A. Goulet.
Material type:
TextPublisher: Cambridge, Massachusetts : The MIT Press, 2020Copyright date: ©2019Description: xxviii, 269 pages : illustrations (some color) ; 26 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9780262538701Subject(s): Machine learning | ProbabilitiesDDC classification: 006.31 LOC classification: Q325.5 | .G68 2020Summary: "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"-- Provided by publisher.
| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
Book
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School of Civil and Environmental Engineering (SCEE) | School of Civil and Environmental Engineering (SCEE) | 006.31 GOU (Browse shelf) | Available | NIT-15535 |
Includes bibliographical references (pages [259]-266) and index.
"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"-- Provided by publisher.
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