000			01804nam a22002057a 4500
999			_c2626 _d2626
005			20220628115800.0
008			220628b ||||| |||| 00| 0 eng d
020			_a9783030723569
082			_a006.3 _bAGG
100			_aAggarwal, Charu C. _94493
245			_aArtificial intelligence: a textbook
260			_bSpringer _aSwitzerland _c2021
300			_axx, 483 p.
365			_aEURO _b54.99
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			_aArtificial intelligence _91478
650			_aMachine learning _92343
650			_aData mining _9365
942			_2ddc _cBK