Artificial Intelligence
A Textbook
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Beschreibung
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.
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.
Teaches artificial intelligence from a broad point of view, while including and integrating multiple schools of thought such as deductive reasoning and inductive learning Provides more balanced coverage, and also discusses how different schools of thought are related to one another, while most artificial intelligence books tend to focus on reasoning methods, treating machine learning in a limited way Newer technologies such as reinforcement learning and knowledge graphs are covered in detail Includes examples and exercises throughout the book Offers a solution manual for teaching instructors only Request lecturer material: sn.pub/lecturer-material
Autor*in
Charu C. Aggarwal
Themen in »Artificial Intelligence«
Artificial Intelligence Machine Learning Deep Learning Neural Networks Data Mining Search Logic Programming Deductive Reasoning Dynamic Programming Reinforcement Learning Probabilistic Graphical Models Knowledge Engineering Transfer Learning
Stimmen zu »Artificial Intelligence«
“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)
“This is very useful book for graduate students and researchers.” (T. C. Mohan, zbMATH 1477.68001, 2022)()