Lecture 15: Probability and Introduction to Uncertainty

This lecture is part of the course “Foundations of Artificial Intelligence” developed by Dr. Ryan Urbanowicz in 2020 at the University of Pennsylvania’s Perelman School of Medicine. This is the first of three courses covering topics in artificial intelligence for application within the context of informatics and biomedical research. The course is divided into modules that cover (1) introductory/background materials, (2) logic, (3) other knowledge representation, (4) essentials of expert systems, (5) search, (6) uncertainty, and (7) advanced/auxiliary topics. These topics offer a global foundation for branches of AI application and research, including concepts that will later support a deeper understanding of inductive reasoning and machine learning. In a practical sense, this course focuses on how biomedical data can be organized, represented, interpreted, searched, and applied in order to derive knowledge, make decisions, and ultimately make predictions while avoiding bias. This course was assembled using content from a wide variety of textbooks, slides, and lectures by various authors and speakers on the relevant topics. Some lectures were prepared and given by guest lecturers and thus have not been posted. At the time of posting, this course is in its second year so any feedback is welcome regarding any mistakes or suggested improvements. Weblinks: http://ryanurbanowicz.com/ https://www.med.upenn.edu/urbslab/ https://github.com/UrbsLab Chapters: 0:00 Introduction 4:18 Basic Probability Theory 6:07 Disjoint Probability 7:21 Mutually Exclusive Events 7:55 Joint Probability 9:59 Conditional Probability 11:31 Getting to Bayes Rule 12:36 The Chain Rule 14:04 Other Probability Terminology 14:57 Statistical Inference with Bayes Rule 23:16 Independence Assumptions 25:51 Conditional Independence 30:28 Reasoning with Multiple Hypotheses 34:46 Likelihood Ratios 40:06 Certainty Factors 49:38 Uncertainty in Expert Systems 54:23 Conclusion