國立高雄大學圖資館 |

Language: English

Back

Artificial intelligencea textbook /

Aggarwal, Charu C.

Artificial intelligencea textbook /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligenceby Charu C. Aggarwal.
Reminder of title:	a textbook /
Author:	Aggarwal, Charu C.
Published:	Cham :Springer International Publishing :2021.
Description:	xx, 483 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence.
Online resource:	https://doi.org/10.1007/978-3-030-72357-6
ISBN:	9783030723576$q(electronic bk.)

Artificial intelligencea 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$q(electronic bk.)

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

194058
Artificial intelligence.

LC Class. No.: Q335 / .A44 2021

Dewey Class. No.: 006.3

Artificial intelligencea textbook /
LDR:02607nmm a2200325 a 4500 001 605274
003 DE-He213
005 20210716224714.0
006 m d
007 cr nn 008maaau
008 211201s2021 sz s 0 eng d
020 $a 9783030723576$q(electronic bk.)
020 $a 9783030723569$q(paper)
024 7 $a 10.1007/978-3-030-72357-6 $2 doi
035 $a 978-3-030-72357-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q335 $b .A44 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
090 $a Q335 $b .A266 2021
100 1 $a Aggarwal, Charu C. $3 264940
245 1 0 $a Artificial intelligence $h [electronic resource] : $b a textbook / $c by Charu C. Aggarwal.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xx, 483 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Artificial intelligence. $3 194058
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-72357-6
950 $a Computer Science (SpringerNature-11645)