Learn about probabilistic models for relational domains in this comprehensive lecture by Daphne Koller from the Center for Language & Speech Processing at Johns Hopkins University. Explore advanced techniques for modeling complex relationships and dependencies in data structures where entities are interconnected, moving beyond traditional flat data representations. Discover how probabilistic graphical models can be extended to handle relational data, including methods for representing uncertainty in domains with rich object structures and relationships. Examine theoretical foundations and practical applications of relational probabilistic models, understanding how these approaches enable more sophisticated reasoning about interconnected systems. Gain insights into the mathematical frameworks that underpin relational modeling, including how to handle the challenges of varying domain sizes and complex dependency structures that arise in real-world relational data.