Introduction to Probabilistic Graphical Models

In a world of uncertain data, making reliable predictions requires more than just simple statistics. Probabilistic graphical models (PGMs) provide a powerful framework for representing complex joint distributions over many variables, enabling systems to reason under uncertainty. By the end of this course, you will understand how to structure, read, and analyze graphical models to solve reasoning problems in fields like medical diagnosis, fault detection, and risk prediction. What you'll learn: - Understand foundational concepts of probability, conditional independence, and graph theory. - Represent directed causal relationships in decision systems using Bayesian networks. - Explore Markov networks for undirected relationships and spatial reasoning. - Apply inference techniques to calculate probabilities and make predictions. - Discover modern applications of graphical models in machine learning and causal inference. This written course starts with essential terminology, basic concepts, and foundational definitions before moving into the practical mechanics of representation and inference. It is designed for beginners in data science, computer science, or analytics, requiring no prior experience with graphical models. Start reading today to build a strong foundation in probabilistic reasoning.