000 04323nam a22005415i 4500
001 978-3-031-66253-9
003 DE-He213
005 20251030113134.0
007 cr nn 008mamaa
008 241009s2024 sz | s |||| 0|eng d
020 _a9783031662539
_9978-3-031-66253-9
024 7 _a10.1007/978-3-031-66253-9
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
245 1 0 _aStatistical Machine Learning for Engineering with Applications
_h[electronic resource] /
_cedited by Jürgen Franke, Anita Schöbel.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aVIII, 392 p. 118 illus., 102 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Statistics,
_x2197-7186 ;
_v227
505 0 _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.
520 _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.
650 0 _aStatistics.
_965982
650 0 _aMachine learning.
_955059
650 0 _aStatistics .
_965982
650 1 4 _aStatistical Theory and Methods.
650 2 4 _aMachine Learning.
_955059
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_966911
700 1 _aFranke, Jürgen.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_985802
700 1 _aSchöbel, Anita.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_985803
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031662522
776 0 8 _iPrinted edition:
_z9783031662546
830 0 _aLecture Notes in Statistics,
_x2197-7186 ;
_v227
_985804
856 4 0 _uhttps://doi.org/10.1007/978-3-031-66253-9
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c64085
_d64085