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Artificial intelligence = a textbook /

Aggarwal, Charu C.

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Artificial intelligence = a textbook /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Artificial intelligence/ by Charu C. Aggarwal.
其他題名:	a textbook /
作者:	Aggarwal, Charu C.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xx, 483 p. :ill., digital ;24 cm.
內容註:	1 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.
Contained By:	Springer Nature eBook
標題:	Artificial intelligence. -
電子資源:	https://doi.org/10.1007/978-3-030-72357-6
ISBN:	9783030723576

Artificial intelligence = a textbook /
Aggarwal, Charu C.

Artificial intelligencea textbook /[electronic resource] :by Charu C. Aggarwal. - Cham :Springer International Publishing :2021. - xx, 483 p. :ill., digital ;24 cm.

1 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.

This 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.

ISBN: 9783030723576

Standard No.: 10.1007/978-3-030-72357-6doiSubjects--Topical Terms:

516317
Artificial intelligence.

LC Class. No.: Q335 / .A44 2021

Dewey Class. No.: 006.3

Artificial intelligence = a textbook /
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505 0 $a 1 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 $a This 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.
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