Probabilistic Graphical Models: Foundations and Computation

Understanding how different variables interact in complex systems is a critical skill in modern data science and artificial intelligence. This text-based course introduces you to the core principles of probabilistic graphical models, combining probability theory with graph theory to solve real-world uncertainty. You will transition from understanding basic probability to constructing, analyzing, and running inference on complex graphical structures. Through clear written explanations, practical code walk-throughs, and structured exercises, you will gain the confidence to represent joint probability distributions efficiently and make data-driven predictions. What you'll learn: 1. Understand foundational concepts of joint probability, conditional independence, and graph theory. 2. Construct Bayesian networks to represent directed causal relationships. 3. Build Markov random fields for undirected graphical representations. 4. Apply exact and approximate inference algorithms to query your models for predictions. 5. Implement probabilistic models using modern Python libraries like pgmpy. 6. Practice structured decision-making under uncertainty. The course begins with essential probability definitions and graph terminology before moving into structural design and computational inference techniques. You will read through detailed conceptual breakdowns and analyze step-by-step code implementations that bring these mathematical models to life. This course is designed for beginners in data analysis, computer science, or statistics who want to understand structured probability models. No advanced mathematical background is required, though basic familiarity with Python is helpful. Start exploring the power of graphical models to make sense of complex systems today.