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.

Bestandsnummer des Verk�ufers 9783031662522

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Bibliografische Details

Titel: Statistical Machine Learning for Engineering with Applications
Verlag: Springer Nature Switzerland, Springer Nature Switzerland
Erscheinungsjahr: 2024
Sprache: Englisch
ISBN-10: 3031662520
ISBN-13: 9783031662522
Einband: Taschenbuch
Zustand: Neu
Abmessungen: Breite: 235 mm, H�he: 155 mm, Tiefe: 22 mm
Gewicht: 604 Gramm

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Inhaltsangabe

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.

�ber die Autorin bzw. den Autor

J�rgen Franke has been a professor of Applied Mathematical Statistics and the speaker of the interdisciplinary Center for Mathematical and Computational Modelling (CM)� at the University of Kaiserslautern. Since his retirement from teaching, he joined the Fraunhofer Institute for Industrial Mathematics (ITWM) as a scientific consultant on statistics. His research focuses on nonlinear time series analysis, nonparametric statistics and statistical learning with applications in engineering and finance.

Anita Sch�bel is professor of Applied Mathematics at the University Kaiserslautern-Landau and head of the Fraunhofer Institute for Industrial Mathematics (ITWM). Currently she is also president of the Association of European Operational Research Societies (EURO). At Fraunhofer Society she is responsible for the strategic research area Next Generation Computing and vice spokesperson for the quantum computing competence network. Her research focuses on discrete optimization and on robust and multi-objective optimization with applications in transportation.

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