Table of Contents

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

About the Author

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.

Xia Jiang is an associate professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine. She has over 16 years of teaching and research experience using artificial intelligence, machine learning, Bayesian networks, and causal learning to model and solve problems in biology, medicine, and translational science. Dr. Jiang pioneered the application of Bayesian networks and information theory to the task of learning causal interactions such as genetic epistasis from data, and she has conducted innovative research in the areas of cancer informatics, probabilistic medical decision support, and biosurveillance. She is the coauthor of the book Probabilistic Methods for Financial and Marketing Informatics (2007).

Reviews

At many universities courses on arti cial intelligence (AI) are offered, mainly for computer science students. This is very often a bit optimistic since this field also requires a sound mathematical background. Furthermore, there is now an increasing rumor about the problems, dangers etc. that may appear. In this field this textbook is an excellent contribution to avoid these discussions and make artificial intelligence more and more a practicable field!-Christian Postho, St. Augustine