| Description |
1 online resource (349 pages) |
| Series |
Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser |
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Chapman and Hall/CRC Data Mining and Knowledge Discovery Ser
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| Contents |
Cover; Half Title; Series Editor; Title; Copyright; Contents; Preface; Chapter 1 The Fourth Industrial Revolution; 1.1 Introduction; 1.1.1 Industrie 4.0; 1.1.2 Industrial Internet of Things; 1.1.3 Other International Strategies; 1.2 Industry Smartization; 1.2.1 At the Component Level; 1.2.2 At the Machine Level; 1.2.3 At the Production Level; 1.2.4 At the Distribution Level; 1.3 Machine Learning Challenges and Opportunities within Smart Industries; 1.3.1 Impact on Business; 1.3.2 Impact on Technology; 1.3.3 Impact on People; 1.4 Concluding Remarks; Chapter 2 Machine Learning |
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2.1 Introduction2.2 Basic Statistics; 2.2.1 Descriptive Statistics; 2.2.1.1 Visualization and Summary of Univariate Data; 2.2.1.2 Visualization and Summary of Bivariate Data; 2.2.1.3 Visualization and Summary of Multivariate Data; 2.2.1.4 Imputation of Missing Data; 2.2.1.5 Variable Transformation; 2.2.2 Inference; 2.2.2.1 Parameter Point Estimation; 2.2.2.2 Parameter Confidence Estimation; 2.2.2.3 Hypothesis Testing; 2.3 Clustering; 2.3.1 Hierarchical Clustering; 2.3.2 K-Means Algorithm; 2.3.3 Spectral Clustering; 2.3.4 Affinity Propagation; 2.3.5 Probabilistic Clustering |
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2.4 Supervised Classification2.4.1 Model Performance Evaluation; 2.4.1.1 Performance Evaluation Measures; 2.4.1.2 Honest Performance Estimation Methods . .; 2.4.2 Feature Subset Selection; 2.4.3 k-Nearest Neighbors; 2.4.4 Classification Trees; 2.4.5 Rule Induction; 2.4.6 Artificial Neural Networks; 2.4.7 Support Vector Machines; 2.4.8 Logistic Regression; 2.4.9 Bayesian Network Classifiers; 2.4.9.1 Discrete Bayesian Network Classifiers; 2.4.9.2 Continuous Bayesian Network Classifiers . .; 2.4.10 Metaclassifiers; 2.5 Bayesian Networks; 2.5.1 Fundamentals of Bayesian Networks |
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2.5.2 Inference in Bayesian Networks2.5.2.1 Types of Inference; 2.5.2.2 Exact Inference; 2.5.2.3 Approximate Inference; 2.5.3 Learning Bayesian Networks from Data; 2.5.3.1 Learning Bayesian Network Parameters; 2.5.3.2 Learning Bayesian Network Structures; 2.6 Modeling Dynamic Scenarios with Bayesian Networks; 2.6.1 Data Streams; 2.6.2 Dynamic, Temporal and Continuous Time Bayesian Networks; 2.6.3 Hidden Markov Models; 2.6.3.1 Evaluation of the Likelihood of an Observation Sequence; 2.6.3.2 Decoding; 2.6.3.3 Hidden Markov Model Training; 2.7 Machine Learning Tools |
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2.8 The Frontiers of Machine LearningChapter 3 Applications of Machine Learning in Industrial Sectors; 3.1 Energy Sector; 3.1.1 Oil; 3.1.2 Gas; 3.2 Basic Materials Sector; 3.2.1 Chemicals; 3.2.2 Basic Resources; 3.3 Industrials Sector; 3.3.1 Construction and Materials; 3.3.2 Industrial Goods and Services; 3.4 Consumer Services Sector; 3.4.1 Retail; 3.4.2 Media; 3.4.3 Tourism; 3.5 Healthcare Sector; 3.5.1 Cancer; 3.5.2 Neuroscience; 3.5.3 Cardiovascular; 3.5.4 Diabetes; 3.5.5 Obesity; 3.5.6 Bioinformatics; 3.6 Consumer Goods Sector; 3.6.1 Automobiles; 3.6.2 Food and Beverages |
| Summary |
Industrial Applications of Machine Learning shows how machine learning can be applied to address real-world problems in the fourth industrial revolution, and provides the required knowledge and tools to empower readers to build their own solutions based on theory and practice. The book introduces the fourth industrial revolution and its current impact on organizations and society. It explores machine learning fundamentals, and includes four case studies that address a real-world problem in the manufacturing or logistics domains, and approaches machine learning solutions from an application-oriented point of view. The book should be of special interest to researchers interested in real-world industrial problems. Features Describes the opportunities, challenges, issues, and trends offered by the fourth industrial revolution Provides a user-friendly introduction to machine learning with examples of cutting-edge applications in different industrial sectors Includes four case studies addressing real-world industrial problems solved with machine learning techniques A dedicated website for the book contains the datasets of the case studies for the reader's reproduction, enabling the groundwork for future problem-solving Uses of three of the most widespread software and programming languages within the engineering and data science communities, namely R, Python, and Weka |
| Notes |
3.6.3 Personal and Household Goods |
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Print version record |
| Subject |
Machine learning -- Industrial applications
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COMPUTERS -- Database Management -- Data Mining.
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COMPUTERS -- Machine Theory.
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artificial intelligence.
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big data.
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data science.
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fourth industrial revoluntion.
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programming.
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Machine learning -- Industrial applications
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| Form |
Electronic book
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| Author |
Atienza, David
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Diaz-Rozo, Javier
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Ogbechie, Alberto
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Puerto-Santana, Carlos Esteban
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Bielza, Concha
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| ISBN |
9781351128377 |
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135112837X |
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