Machine learning for civil & environmental engineers : a practical approach to data-driven analysis, explainability, and causality / M. Z. Naser.
Material type:
TextPublisher: Hoboken, New Jersey : Wiley, [2023]Copyright date: ©2023Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119897620Subject(s): Machine learning | Civil engineering -- Data processing | Environmental engineering -- Data processingAdditional physical formats: Print version:: Machine learning for civil & environmental engineersDDC classification: 006.3/1 LOC classification: Q325.5Summary: "Synopsis: The theme of this textbook revolves around how machine learning (ML) can help civil and environmental engineers transform their domain. This textbook hopes to deliver the knowledge and information necessary to educate engineering students and practitioners on the principles of ML and how to integrate these into our field. This textbook is about navigating the realm of data-driven ML, explainable ML, and causal ML from the context of education, research, and practice. In hindsight, this textbook augments ML into the heart of engineering. Together, we will go over the big ideas behind ML. We will ask and answer questions such as, what is ML? Why is ML needed? How does ML differ from statistics, physical testing, and numerical simulation? Can we trust ML? And how can we benefit from ML, adapt to it, adopt it, wield it, and leverage it to overcome many, many of the problems that we may face? This book is also about showing you, my dear reader, how to amplify your engineering knowledge with a new tool. A tool that is yet to be formally taught in our curriculum. A tool that many civil and environmental engineering departments and schools may not fully appreciate ; yet are eager to know more about!"-- Provided by publisher.
| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
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School of Civil and Environmental Engineering (SCEE) | School of Civil and Environmental Engineering (SCEE) | 006.31 (Browse shelf) | Available | NIT-13768 |
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Includes bibliographical references and index.
"Synopsis: The theme of this textbook revolves around how machine learning (ML) can help civil and environmental engineers transform their domain. This textbook hopes to deliver the knowledge and information necessary to educate engineering students and practitioners on the principles of ML and how to integrate these into our field. This textbook is about navigating the realm of data-driven ML, explainable ML, and causal ML from the context of education, research, and practice. In hindsight, this textbook augments ML into the heart of engineering. Together, we will go over the big ideas behind ML. We will ask and answer questions such as, what is ML? Why is ML needed? How does ML differ from statistics, physical testing, and numerical simulation? Can we trust ML? And how can we benefit from ML, adapt to it, adopt it, wield it, and leverage it to overcome many, many of the problems that we may face? This book is also about showing you, my dear reader, how to amplify your engineering knowledge with a new tool. A tool that is yet to be formally taught in our curriculum. A tool that many civil and environmental engineering departments and schools may not fully appreciate ; yet are eager to know more about!"-- Provided by publisher.
Description based on print version record and CIP data provided by publisher; resource not viewed.
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