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020 _a9789811681936
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024 7 _a10.1007/978-981-16-8193-6
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
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072 7 _aUYQM
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082 0 4 _a006.31
_223
100 1 _aJung, Alexander.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning
_h[electronic resource] :
_bThe Basics /
_cby Alexander Jung.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXVII, 212 p. 77 illus., 42 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 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
505 0 _aIntroduction -- Components of ML -- The Landscape of ML -- Empirical Risk Minimization -- Gradient-Based Learning -- Model Validation and Selection -- Regularization -- Clustering -- Feature Learning -- Transparant and Explainable ML.
520 _aMachine learning (ML) has become a commonplace element in our everyday lives and a standard tool for many fields of science and engineering. To make optimal use of ML, it is essential to understand its underlying principles. This book approaches ML as the computational implementation of the scientific principle. This principle consists of continuously adapting a model of a given data-generating phenomenon by minimizing some form of loss incurred by its predictions. The book trains readers to break down various ML applications and methods in terms of data, model, and loss, thus helping them to choose from the vast range of ready-made ML methods. The book’s three-component approach to ML provides uniform coverage of a wide range of concepts and techniques. As a case in point, techniques for regularization, privacy-preservation as well as explainability amount tospecific design choices for the model, data, and loss of a ML method. .
650 0 _aMachine learning.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aData mining.
650 1 4 _aMachine Learning.
650 2 4 _aData Science.
650 2 4 _aArtificial Intelligence.
650 2 4 _aModels of Computation.
650 2 4 _aData Mining and Knowledge Discovery.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811681929
776 0 8 _iPrinted edition:
_z9789811681943
776 0 8 _iPrinted edition:
_z9789811681950
830 0 _aMachine Learning: Foundations, Methodologies, and Applications,
_x2730-9916
856 4 0 _uhttps://doi.org/10.1007/978-981-16-8193-6
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c184502
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