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008			210820s2021 si | s |||| 0|eng d
020			_a9789811519673
024	7		_a10.1007/978-981-15-1967-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100	1		_aZhou, Zhi-Hua _eautor _9681254 _c(Computer scientist)
245	1	0	_aMachine Learning _cby Zhi-Hua Zhou
250			_aFirst edition 2021
264		1	_aSingapore _bSpringer International Publising _c2021
300			_a1 recurso en línea (XIII, 459 páginas) _b137 ilustraciones, 68 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aComputer Science (SpringerNature-11645)
490	0		_aComputer Science (R0) (SpringerNature-43710)
505	0		_a1 Introduction -- 2 Model Selection and Evaluation -- 3 Linear Models -- 4 Decision Trees -- 5 Neural Networks -- 6 Support Vector Machine -- 7 Bayes Classifiers -- 8 Ensemble Learning -- 9 Clustering -- 10 Dimensionality Reduction and Metric Learning -- 11 Feature Selection and Sparse Learning -- 12 Computational Learning Theory -- 13 Semi-Supervised Learning -- 14 Probabilistic Graphical Models -- 15 Rule Learning -- 16 Reinforcement Learning.
520	3		_aMachine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.
988			_aSpringer_Computer_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-15-1967-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eu _zSI