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Artificial Intelligence

- A Textbook
Af: Charu C. Aggarwal Engelsk Paperback

Artificial Intelligence

- A Textbook
Af: Charu C. Aggarwal Engelsk Paperback
Tjek vores konkurrenters priser

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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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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Se mere i:
Produktdetaljer
Sprog: Engelsk
Sider: 483
ISBN-13: 9783030723590
Indbinding: Paperback
Udgave:
ISBN-10: 3030723593
Kategori: Data mining
Udg. Dato: 18 jul 2022
Længde: 31mm
Bredde: 255mm
Højde: 177mm
Forlag: Springer Nature Switzerland AG
Oplagsdato: 18 jul 2022
Forfatter(e): Charu C. Aggarwal
Forfatter(e) Charu C. Aggarwal

Kategori Data mining

ISBN-13 9783030723590

Sprog Engelsk

Indbinding Paperback

Sider 483

Udgave

Længde 31mm

Bredde 255mm

Højde 177mm

Udg. Dato 18 jul 2022

Oplagsdato 18 jul 2022

Forlag Springer Nature Switzerland AG

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