Artificial Intelligence
A Textbook
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Beszerezhetőség
Becsült beszerzési idő: A Prosperónál jelenleg nincsen raktáron, de a kiadónál igen. Beszerzés kb. 3-5 hét..
A Prosperónál jelenleg nincsen raktáron.
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A termék adatai:
- Kiadás sorszáma 1st ed. 2021
- Kiadó Springer
- Megjelenés dátuma 2022. július 18.
- Kötetek száma 1 pieces, Book
- ISBN 9783030723590
- Kötéstípus Puhakötés
- Terjedelem483 oldal
- Méret 254x178 mm
- Súly 957 g
- Nyelv angol
- Illusztrációk 158 Illustrations, black & white; 15 Illustrations, color 438
Kategóriák
Rövid leírás:
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.
TöbbHosszú leírás:
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.
?The author has thoroughly researched all areas of AI in order to write this high-quality book. ? This highly valuable book provides a vast overview of AI in a well-structured manner. It could be used as a textbook in graduate-level courses.? (J. Arul, Computing Reviews, December 12, 2022)
Tartalomjegyzék:
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.
Több
