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Big Data Analytics for Cyber-Physical Systems : Machine Learning for the Internet of Things by Guido Dartmann (editor), Houbing Herbert Song PhD (editor), Anke Schmeink (editor) is a nonfiction available to read on EtoBox.

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Front Cover Big Data Analytics forCyber-Physical Systems: Machine Learning for the Internet of Things Copyright Contents Contributors Foreword Acknowledgments Introduction Chapter 1: Data analytics and processing platforms in CPS 1. Open source versus proprietary software 2. Data types 3. Easy data visualization using code 4. Statistical measurements in CPS data 5. Statistical methods, models, and techniques: Brief introduction 6. Analytics and statistics versus ML techniques 7. Data charts 8. Machine logs analysis and dashboarding 9. Conclusion References Chapter 2: Fundamentals of data analysis and statistics 1. Introduction 2. Useful software tools 2.1. Software for statistical computations 2.2. Software for interactive graphical data representation 3. Fundamentals of statistics 3.1. Features and scalings 3.1.1. Nominal, ordinal, and metric features 3.1.2. Discrete and continuous features 3.2. Characterization of univariate distributions 3.2.1. Nominal features 3.2.2. Ordinal features and quantiles 3.2.3. Metric features-Grapical representation 3.2.4. Metric features-Characteristic numbers 3.3. Characterization of multivariate distributions 4. Regression: Fitting functional mode

Who reads Big Data Analytics for Cyber-Physical Systems : Machine Learning for the Internet of Things?

Author

Guido Dartmann (editor), Houbing Herbert Song PhD (editor), Anke Schmeink (editor)

Publisher

Elsevier

Published

2019

Language

EN

ISBN

9780128166468

Category

nonfiction

Subjects

Law, Computer Science