000 | 03261nam a22005895i 4500 | ||
---|---|---|---|
001 | 978-981-16-8193-6 | ||
003 | DE-He213 | ||
005 | 20240423130041.0 | ||
007 | cr nn 008mamaa | ||
008 | 220121s2022 si | s |||| 0|eng d | ||
020 |
_a9789811681936 _9978-981-16-8193-6 |
||
024 | 7 |
_a10.1007/978-981-16-8193-6 _2doi |
|
050 | 4 | _aQ325.5-.7 | |
072 | 7 |
_aUYQM _2bicssc |
|
072 | 7 |
_aMAT029000 _2bisacsh |
|
072 | 7 |
_aUYQM _2thema |
|
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 _d184502 |