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008			220121s2022 si | s |||| 0|eng d
020			_a9789811681936
024	7		_a10.1007/978-981-16-8193-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aJung, Alexander _eautor _9683336
245	1	0	_aMachine Learning : _bThe Basics _cby Alexander Jung
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XVII, 212 páginas) _b77 ilustraciones, 42 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_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 to specific design choices for the model, data, and loss of a ML method.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático
773	0		_tSpringer Nature eBook
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-8193-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE _n0
998			_b03/2022 _dz _eu _zSI