Statistical Machine Learning Engineering (17 results)
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Published by Springer, 2024
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Published by Springer, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Published by Springer, 2024
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Published by Springer International Publishing AG, Cham, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Paperback. Condition: new. Paperback. This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided a…s far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed.The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis.The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Published by Createspace Independent Publishing Platform Mai 2026, 2026
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Language: English
Published by Springer, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Taschenbuch. Condition: Neu. Statistical Machine Learning for Engineering with Applications | Jürgen Franke (u. a.) | Taschenbuch | Lecture Notes in Statistics | viii | Englisch | 2024 | Springer | EAN 9783031662522 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartma…nn[at]springer[dot]com | Anbieter: preigu.
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Published by Springer Nature, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Published by Springer, Palgrave Macmillan, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease… of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed.The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis.The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.
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Published by Springer, Palgrave Macmillan Okt 2024, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related m…ethods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed.The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis.The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students. 400 pp. Englisch.
Language: English
Published by Springer, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Published by Springer, Berlin|Springer Nature Switzerland|Springer, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments…, and related methods. For ease of re.
Language: English
Published by Springer, Springer Okt 2024, 2024
Series: Lecture Notes in Statistics, Book 72 of 72. Book 72 of 72 - Lecture Notes in Statistics
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related metho…ds. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed.The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis.The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 400 pp. Englisch.
