Technoscience in History
Prussia, 1750-1850
Published on 22. September 2020
304 pages
978-0-262-35948-1 (ISBN)
Description
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
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09/2020
MIT Press
€45.60
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Person
John D. Kelleher is Academic Leader of the Information, Communication, and Entertainment Research Institute at Technological University Dublin. Brian Mac Namee is Associate Professor at the School of Computer Science at University College Dublin. Aoife D'Arcy is CEO of Krisolis, a data analytics company based in Dublin.
Content
List of Illustrations
Acknowledgments
Introduction
Part I. Technical Experts and Innovation in Prussia
1. Technical Projects of the Royal Prussian Academy of Sciences
2. Discovery and Invention: Klaproth
3. Pharmacy and Chemistry
4. Experts at the Royal Prussian Porcelain Manufactory
5. The Figure of Technical Expert
Part II. The Model: Useful Science at Mining Academies
6. Silver Mining and the Freiberg Mining Academy
7. Mining and Mining Experts in Prussia: Gerhard
8. Experiments in the Laboratory of the Mining Department
9. The Lecture Series of the Mining Administration
10. Mines as Laboratories: Humboldt
Part III. Useful Science and its Practitioners
11. Mining Science
12. The Science of Salt Works
13. The Figure of Scientific-Technological Expert
Part IV. Towards Nineteenth-Century Technological Science
14. Useful Knowledge at the University of Berlin
15. The Academy of Civil Engineering and Architecture
16. A New Industrial Policy: The Industrial Institute
17. The Big Picture: Useful Science, Technological Science, Technoscience
Notes
References
Index
Acknowledgments
Introduction
Part I. Technical Experts and Innovation in Prussia
1. Technical Projects of the Royal Prussian Academy of Sciences
2. Discovery and Invention: Klaproth
3. Pharmacy and Chemistry
4. Experts at the Royal Prussian Porcelain Manufactory
5. The Figure of Technical Expert
Part II. The Model: Useful Science at Mining Academies
6. Silver Mining and the Freiberg Mining Academy
7. Mining and Mining Experts in Prussia: Gerhard
8. Experiments in the Laboratory of the Mining Department
9. The Lecture Series of the Mining Administration
10. Mines as Laboratories: Humboldt
Part III. Useful Science and its Practitioners
11. Mining Science
12. The Science of Salt Works
13. The Figure of Scientific-Technological Expert
Part IV. Towards Nineteenth-Century Technological Science
14. Useful Knowledge at the University of Berlin
15. The Academy of Civil Engineering and Architecture
16. A New Industrial Policy: The Industrial Institute
17. The Big Picture: Useful Science, Technological Science, Technoscience
Notes
References
Index
