{"product_id":"machine-learning-for-civil-and-environmental-engineers-9781119897606","title":"Machine Learning for Civil and Environmental","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Teaching Methods for This Textbook 1 Synopsis 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Education in Civil and Environmental Engineering 1\u003c\/p\u003e \u003cp\u003e1.2 Machine Learning as an Educational Material 2\u003c\/p\u003e \u003cp\u003e1.3 Possible Pathways for Course\/Material Delivery 3\u003c\/p\u003e \u003cp\u003e1.4 Typical Outline for Possible Means of Delivery 7\u003c\/p\u003e \u003cp\u003eChapter Blueprint 8\u003c\/p\u003e \u003cp\u003eQuestions and Problems 8\u003c\/p\u003e \u003cp\u003eReferences 8\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction to Machine Learning 11\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 11\u003c\/p\u003e \u003cp\u003e2.1 A Brief History of Machine Learning 11\u003c\/p\u003e \u003cp\u003e2.2 Types of Learning 12\u003c\/p\u003e \u003cp\u003e2.3 A Look into ML from the Lens of Civil and Environmental Engineering 15\u003c\/p\u003e \u003cp\u003e2.4 Let Us Talk a Bit More about ML 17\u003c\/p\u003e \u003cp\u003e2.5 ML Pipeline 18\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 27\u003c\/p\u003e \u003cp\u003eDefinitions 27\u003c\/p\u003e \u003cp\u003eChapter Blueprint 29\u003c\/p\u003e \u003cp\u003eQuestions and Problems 29\u003c\/p\u003e \u003cp\u003eReferences 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Data and Statistics 33\u003c\/b\u003e \u003cbr\u003e\u003cbr\u003eSynopsis 33\u003c\/p\u003e \u003cp\u003e3.1 Data and Data Science 33\u003c\/p\u003e \u003cp\u003e3.2 Types of Data 34\u003c\/p\u003e \u003cp\u003e3.3 Dataset Development 37\u003c\/p\u003e \u003cp\u003e3.4 Diagnosing and Handling Data 37\u003c\/p\u003e \u003cp\u003e3.5 Visualizing Data 38\u003c\/p\u003e \u003cp\u003e3.6 Exploring Data 59\u003c\/p\u003e \u003cp\u003e3.7 Manipulating Data 66\u003c\/p\u003e \u003cp\u003e3.8 Manipulation for Computer Vision 68\u003c\/p\u003e \u003cp\u003e3.9 A Brief Review of Statistics 68\u003c\/p\u003e \u003cp\u003e3.10 Conclusions 76\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Machine Learning Algorithms 81\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 81\u003c\/p\u003e \u003cp\u003e4.1 An Overview of Algorithms 81\u003c\/p\u003e \u003cp\u003e4.2 Conclusions 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Performance Fitness Indicators and Error Metrics 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 133\u003c\/p\u003e \u003cp\u003e5.1 Introduction 133\u003c\/p\u003e \u003cp\u003e5.2 The Need for Metrics and Indicators 134\u003c\/p\u003e \u003cp\u003e5.3 Regression Metrics and Indicators 135\u003c\/p\u003e \u003cp\u003e5.4 Classification Metrics and Indicators 142\u003c\/p\u003e \u003cp\u003e5.5 Clustering Metrics and Indicators 142\u003c\/p\u003e \u003cp\u003e5.6 Functional Metrics and Indicators* 151\u003c\/p\u003e \u003cp\u003e5.7 Other Techniques (Beyond Metrics and Indicators) 154\u003c\/p\u003e \u003cp\u003e5.8 Conclusions 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Coding-free and Coding-based Approaches to Machine Learning 169\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 169\u003c\/p\u003e \u003cp\u003e6.1 Coding-free Approach to ML 169\u003c\/p\u003e \u003cp\u003e6.2 Coding-based Approach to ML 280\u003c\/p\u003e \u003cp\u003e6.3 Conclusions 322\u003c\/p\u003e \u003cp\u003e7 Explainability and Interpretability 327\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Synopsis 327\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 The Need for Explainability 327\u003c\/p\u003e \u003cp\u003e7.2 Explainability from a Philosophical Engineering Perspective* 329\u003c\/p\u003e \u003cp\u003e7.3 Methods for Explainability and Interpretability 331\u003c\/p\u003e \u003cp\u003e7.4 Examples 335\u003c\/p\u003e \u003cp\u003e7.5 Conclusions 428\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Causal Discovery and Causal Inference 433\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 433\u003c\/p\u003e \u003cp\u003e8.1 Big Ideas Behind This Chapter 433\u003c\/p\u003e \u003cp\u003e8.2 Re-visiting Experiments 434\u003c\/p\u003e \u003cp\u003e8.3 Re-visiting Statistics and ML 435\u003c\/p\u003e \u003cp\u003e8.4 Causality 436\u003c\/p\u003e \u003cp\u003e8.5 Examples 451\u003c\/p\u003e \u003cp\u003e8.6 A Note on Causality and ML 475\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 475\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Advanced Topics (Synthetic and Augmented Data, Green ML, Symbolic Regression, Mapping Functions, Ensembles, and AutoML) 481\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 481\u003c\/p\u003e \u003cp\u003e9.1 Synthetic and Augmented Data 481\u003c\/p\u003e \u003cp\u003e9.2 Green ML 488\u003c\/p\u003e \u003cp\u003e9.3 Symbolic Regression 498\u003c\/p\u003e \u003cp\u003e9.4 Mapping Functions 529\u003c\/p\u003e \u003cp\u003e9.5 Ensembles 539\u003c\/p\u003e \u003cp\u003e9.6 AutoML 548\u003c\/p\u003e \u003cp\u003e9.7 Conclusions 552\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Recommendations, Suggestions, and Best Practices 559\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 559\u003c\/p\u003e \u003cp\u003e10.1 Recommendations 559\u003c\/p\u003e \u003cp\u003e10.2 Suggestions 564\u003c\/p\u003e \u003cp\u003e10.3 Best Practices 566\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Final Thoughts and Future Directions 573\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSynopsis 573\u003c\/p\u003e \u003cp\u003e11.1 Now 573\u003c\/p\u003e \u003cp\u003e11.2 Tomorrow 573\u003c\/p\u003e \u003cp\u003e11.3 Possible Ideas to Tackle 575\u003c\/p\u003e \u003cp\u003e11.4 Conclusions 576\u003c\/p\u003e \u003cp\u003eReferences 576\u003c\/p\u003e Index 577","brand":"John Wiley \u0026 Sons 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