Artificial Intelligence

Richard E. Neapolitan is professor emeritus of computer science at Northeastern Illinois University and a former professor of bioinformatics at Northwestern University. He is currently president of Bayesian Network Solutions. His research interests include probability and statistics, decision support systems, cognitive science, and applications of probabilistic modeling to fields such as medicine, biology, and finance. Dr. Neapolitan is a prolific author and has published in the most prestigious journals in the broad area of reasoning under uncertainty. He has previously written five books, including the seminal 1989 Bayesian network text Probabilistic Reasoning in Expert Systems; Learning Bayesian Networks (2004); Foundations of Algorithms (1996, 1998, 2003, 2010, 2015), which has been translated into three languages; Probabilistic Methods for Financial and Marketing Informatics (2007); and Probabilistic Methods for Bioinformatics (2009). His approach to textbook writing is distinct in that he introduces a concept or methodology with simple examples, and then provides the theoretical underpinning. As a result, his books have the reputation for making difficult material easy to understand without sacrificing scientific rigor.

The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update retains the same accessibility and problem-solving approach, while providing new material and methods, including neural networks and deep learning.

1. Introduction to Artificial Intelligence Part 1: Logical Intelligence 2. Propositional Logic 3. First-Order Logic 4. Certain Knowledge Representation 5. Learning Deterministic Models Part 2: Probabilistic Intelligence 6. Probability 7. Uncertain Knowledge Representation 8. Advanced Properties of Bayesian Network 9. Decision Analysis 10. Learning Probabilistic Model Parameters 11. Learning Probabilistic Model Structure 12. Unsupervised Learning and Reinforcement Learning Part 3: Emergent Intelligence 13. Evolutionary Computation 14. Swarm Intelligence Part 4: Neural Intelligence 15. Neural Networks and Deep Learning Part 5: Language Understanding 16. Natural Language Understanding