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 : John Wiley & Sons, [2023]Description: xix, 588 pages : illustrationsContent type: - text
- unmediated
- volume
- 9781119897606
- Machine learning for civil and environmental engineers
- 006.31
| Item type | Current library | Collection | Call number | Copy number | Status | Barcode | |
|---|---|---|---|---|---|---|---|
Book
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Lagro Branch Reference Section | Reference | R 006.31 N247m 2023 (Browse shelf(Opens below)) | c. 2 | Room use only | 57745QC | |
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Book
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Main Library Reference Section | Reference | R 006.31 N247m 2023 (Browse shelf(Opens below)) | c. 1 | Room use only | 57744QC |
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| R 005.8 R351d 2023 Data governance for dummies | R 005.8 Si617a 2023 Advance cyber security | R 005.824 M265c 2024 Cryptography : algorithms, protocols, and standards for computer security | R 006.31 N247m 2023 Machine learning for civil & environmental engineers : a practical approach to data-driven analysis, explainability, and causality | R 006.66 Si617c 2022 Computer graphic science | R 006.74 W629b 2003 Basic HTML | R 016.1581 B986f 2017 50 success classics : your shortcut to the most important ideas on motivation, achievement, and prosperity |
Includes bibliographical references and index.
Teaching methods for this textbook
Introduction to machine learning
Data and statistics
Machine learning algorithms
Performance fitness indicators and error metrics
Coding-free and coding-based approaches to machine learning
Explainability and interpretability
Causal discovery and casual inference
Final thoughts and future directions
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