Foundations of Probabilistic Graphical Models

Modeling uncertainty is one of the greatest challenges in modern data science and artificial intelligence. Probabilistic Graphical Models (PGMs) provide a powerful framework to represent complex relationships between variables using intuitive graph structures. In this text-based course, you will transition from basic probability concepts to designing structured representations of multi-variable systems. By learning how to encode conditional independence assumptions, you will be able to construct robust models for decision-making, diagnostics, and predictive analysis. What you'll learn: - Understand foundational probability theory, graph concepts, and conditional independence. - Build directed graphical models using Bayesian networks to represent causal relationships. - Configure undirected graphical models using Markov networks for symmetric interactions. - Analyze the local and global independence properties encoded within graph structures. - Explore how structured representation concepts underpin modern generative AI and probabilistic programming frameworks. The course begins with essential terminology and fundamental probability definitions before guiding you through the mechanics of directed and undirected graphs. You will read clear explanations, walk through structured mathematical formulations, and study practical representations of complex distributions. This course is designed for beginners in machine learning and data science who want to understand the structural side of probabilistic modeling, with no advanced background in graphical models required. Start reading today to master the structural foundations of probabilistic reasoning.