04323nam a22005415i 4500 978-3-031-66253-9 DE-He213 20251030113134.0 cr nn 008mamaa 241009s2024 sz | s |||| 0|eng d 9783031662539 978-3-031-66253-9 10.1007/978-3-031-66253-9 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Statistical Machine Learning for Engineering with Applications [electronic resource] / edited by Jürgen Franke, Anita Schöbel. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. VIII, 392 p. 118 illus., 102 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Lecture Notes in Statistics, 2197-7186 ; 227 - An Introduction of Statistical Learning for Engineers -- Machine Learning for Inline Surface Inspection Systems - Challenges, Approaches, and Application Example -- Gaussian Process Regression for the Prediction of Cable Bundle Characteristics -- Machine Learning for Predictive Maintenance in Production Environments -- Detecting Healthcare Fraud Using Hybrid Machine Learning for Document Digitization -- Cracks in concrete -- Machine learning methods for prediction of breakthrough curves in reactive porous media -- Segmentation and Aggregation in Text Classification -- Hardware-aware Neural Architecture Search -- Optimal Experimental Design Supported by Machine Learning Regression Models -- Data Analytics, Artificial Intelligence and Machine Learning in Mobility and Vehicle Engineering. 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. Statistics. 65982 Machine learning. 55059 Statistics . 65982 Statistical Theory and Methods. Machine Learning. 55059 Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 66911 Franke, Jürgen. editor. edt http://id.loc.gov/vocabulary/relators/edt 85802 Schöbel, Anita. editor. edt http://id.loc.gov/vocabulary/relators/edt 85803 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031662522 Printed edition: 9783031662546 Lecture Notes in Statistics, 2197-7186 ; 227 85804 https://doi.org/10.1007/978-3-031-66253-9 ZDB-2-SMA ZDB-2-SXMS 64085 64085