ARTIFICIAL INTELLIGENCE (AI), A TEXTBOOK
This book covers the broader field of AI, carefully balancing coverage between classical AI (logic or deductive reasoning) and modern AI (inductive learning and neural networks).
Artificial Intelligence: A Textbook (Springer),
by Charu C. Aggarwal, June 2021
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This book covers the broader field of artificial intelligence. The book carefully balances coverage between classical AI (logic or deductive reasoning) and modern AI (inductive learning and neural networks). The chapters of this book span 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 1 through 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 12 and 13 discuss techniques for integrating reasoning and learning. Examples include the use of knowledge graphs and neuro-symbolic artificial intelligence.
The book is available in both hardcopy (hardcover) and electronic versions.
The hardcover is available at all the usual channels (e.g, Amazon, Barnes and Noble etc.), in Kindle format, and also directly from Springer in hardcopy and pdf format. PDF versions do have links and work with e-readers (including the kindle reader). The PDF version (bought directly from Springer) provides better formatting of equations than the kindle version and has an almost identical layout and pagination to the hardcopy on the e-reader.