東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Artificial intelligence = a textbook /

Aggarwal, Charu C.

Linked to FindBook

Google Book

Amazon

博客來

Artificial intelligence = a textbook /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence/ by 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.
[NT 15003449]:	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
Subject:	Artificial intelligence. -
Online resource:	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 /
LDR:02607nmm a2200325 a 4500 001 2242059
003 DE-He213
005 20210716224714.0
006 m d
007 cr nn 008maaau
008 211207s2021 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 812147
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 516317
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
710 2 $a SpringerLink (Online service) $3 836513
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)