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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