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Sep 22, 2020
| ISBN 9780262539296
Sep 22, 2020
| ISBN 9780262359481
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Sep 22, 2020 | ISBN 9780262539296
Sep 22, 2020 | ISBN 9780262359481
The relationship of the current technosciences and the older engineering sciences, examined through the history of the “useful” sciences in Prussia.
Do today’s technoscientific disciplines—including materials science, genetic engineering, nanotechnology, and robotics—signal a radical departure from traditional science? In Technoscience in History, Ursula Klein argues that these novel disciplines and projects are not an “epochal break,” but are part of a history that can be traced back to German “useful” sciences and beyond. Klein’s account traces a deeper history of technoscience, mapping the relationship between today’s cutting-edge disciplines and the development of the useful and technological sciences in Prussia from 1750 to 1850.
Klein shows that institutions that coupled natural-scientific and technological inquiry existed well before the twentieth century. Focusing on the science of mining, technical chemistry, the science of forestry, and the science of building (later known as civil engineering), she examines the emergence of practitioners who were recognized as men of science as well as inventive technologists—key figures that she calls “scientific-technological experts.”
Klein describes the Prussian state’s recruitment of experts for technical projects and manufacturing, including land surveys, the apothecary trade, and porcelain production; state-directed mining, mining science, and mining academies; the history and epistemology of useful science; and the founding of Prussian scientific institutions in the nineteenth century, including the University of Berlin, the Academy of Building, the Technical Deputation, and the Industrial Institute.
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.
“The term ‘technoscience’ may feel futuristic, but Ursula Klein’s fascinating study shows that the phenomenon extends back centuries. She demonstrates in detail that mining, among other key fields, involved the kind of ‘useful knowledge’ that spurred modern science.” – Michael D. Gordin, Rosengarten Professor of Modern and Contemporary History, Princeton University “Technoscience in History imaginatively explores the role of useful sciences in Prussia’s knowledge economy. It recasts several canonical historical narratives: of industrialization, state expertise, and even Berlin University’s founding. It adds incredible historical depth to Bruno Latour’s Science in Action.” – Kathryn Olesko, Associate Professor, George Washington University
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