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001			978-3-030-72357-6
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020			_a9783030723576 _9978-3-030-72357-6
024	7		_a10.1007/978-3-030-72357-6 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
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072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aAggarwal, Charu C. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aArtificial Intelligence _h[electronic resource] : _bA Textbook / _cby Charu C. Aggarwal.
250			_a1st ed. 2021.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021.
300			_aXX, 483 p. 173 illus., 15 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
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			_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.
650		0	_aArtificial intelligence.
650		0	_aMachine learning.
650		0	_aData mining.
650	1	4	_aArtificial Intelligence.
650	2	4	_aMachine Learning.
650	2	4	_aData Mining and Knowledge Discovery.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783030723569
776	0	8	_iPrinted edition: _z9783030723583
776	0	8	_iPrinted edition: _z9783030723590
856	4	0	_uhttps://doi.org/10.1007/978-3-030-72357-6
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_cSPRINGER
999			_c184893 _d184893