Bayesian Networks - Selected Lectures

Explore the fundamentals of Bayesian networks through this comprehensive lecture series from UCLA's Automated Reasoning Group, taught by Professor Adnan Darwiche. Master propositional logic foundations before diving into probability calculus with beliefs and hard evidence. Learn Bayesian network syntax, semantics, independence properties, and d-separation techniques essential for understanding probabilistic reasoning. Discover practical approaches to building Bayesian networks and perform inference using variable elimination methods. Advance to parameter learning techniques for both complete and incomplete data scenarios. Conclude with advanced topics in causality, including interventions and counterfactuals, providing the theoretical foundation needed for causal reasoning. Each lecture builds systematically on previous concepts, creating a solid mathematical and conceptual framework for probabilistic graphical models and their applications in artificial intelligence and machine learning.