LEADER 04108nam 22005775 450 001 9910896181303321 005 20250418030325.0 010 $a3-031-66253-9 024 7 $a10.1007/978-3-031-66253-9 035 $a(CKB)36315578300041 035 $a(MiAaPQ)EBC31717037 035 $a(Au-PeEL)EBL31717037 035 $a(DE-He213)978-3-031-66253-9 035 $a(EXLCZ)9936315578300041 100 $a20241009d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Machine Learning for Engineering with Applications /$fedited by Jürgen Franke, Anita Schöbel 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (393 pages) 225 1 $aLecture Notes in Statistics,$x2197-7186 ;$v227 311 08$a3-031-66252-0 327 $a- 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. 330 $aThis book offers a leisurely introduction to the concepts and methods of machine learning. Readers will learn about classification trees, Bayesian learning, neural networks and deep learning, the design of experiments, and related methods. For ease of reading, technical details are avoided as far as possible, and there is a particular emphasis on applicability, interpretation, reliability and limitations of the data-analytic methods in practice. To cover the common availability and types of data in engineering, training sets consisting of independent as well as time series data are considered. To cope with the scarceness of data in industrial problems, augmentation of training sets by additional artificial data, generated from physical models, as well as the combination of machine learning and expert knowledge of engineers are discussed. The methodological exposition is accompanied by several detailed case studies based on industrial projects covering a broad range of engineering applications from vehicle manufacturing, process engineering and design of materials to optimization of production processes based on image analysis. The focus is on fundamental ideas, applicability and the pitfalls of machine learning in industry and science, where data are often scarce. Requiring only very basic background in statistics, the book is ideal for self-study or short courses for engineering and science students. 410 0$aLecture Notes in Statistics,$x2197-7186 ;$v227 606 $aStatistics 606 $aMachine learning 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aMachine Learning 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aStatistics. 615 0$aMachine learning. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aMachine Learning. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a006.31 700 $aFranke$b Jürgen$0145039 701 $aSchöbel$b Anita$01767577 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910896181303321 996 $aStatistical Machine Learning for Engineering with Applications$94213699 997 $aUNINA