This textbook covers the broader field of artificial intelligence. The
chapters for this textbook span within three categories:
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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.
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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.
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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.