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008			210716s2021 sz | s |||| 0|eng d
020			_a9783030723576
024	7		_a10.1007/978-3-030-72357-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2021 EB
100			_aAggarwal, Charu C. _eautor _998701
245	1	0	_aArtificial Intelligence : _bA Textbook _cby Charu C. Aggarwal
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publising _c2021
300			_a1 recurso en línea (XX, 483 páginas) _b173 ilustraciones, 15 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 An Introduction to Artificial Intelligence -- 2 Searching State Spaces -- 3 Multiagent Search -- 4 Propositional Logic -- 5 First-Order Logic -- 6 Machine Learning: The Inductive View -- 7 Neural Networks -- 8 Domain-Specific Neural Architectures -- 9 Unsupervised Learning -- 10 Reinforcement Learning -- 11 Probabilistic Graphical Models -- 12 Knowledge Graphs -- 13 Integrating Reasoning and Learning
520	3		_aThis textbook covers the broader field of artificial intelligence. The chapters for this textbook span within three categories: Deductive reasoning methods: These methods start with pre-defined hypotheses and reason with them in order to arrive at logically sound conclusions. The underlying methods include search and logic-based methods. These methods are discussed in Chapters 1through 5. Inductive Learning Methods: These methods start with examples and use statistical methods in order to arrive at hypotheses. Examples include regression modeling, support vector machines, neural networks, reinforcement learning, unsupervised learning, and probabilistic graphical models. These methods are discussed in Chapters~6 through 11. Integrating Reasoning and Learning: Chapters~11 and 12 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence. The primary audience for this textbook are professors and advanced-level students in computer science. It is also possible to use this textbook for the mathematics requirements for an undergraduate data science course. Professionals working in this related field many also find this textbook useful as a reference.
988			_aSpringer_Computer_2021
650		7	_2embne _aInteligencia artificial _9413115
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-72357-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2022 _dz _eu _zSI