Probabilistic graphical models are widely used in medical diagnosis, fault detection, and risk prediction systems where calibrated probabilistic reasoning is critical for decision support. This Short Course was created to help Machine Learning and Artificial Intelligence professionals accomplish building robust inference systems that handle uncertainty with mathematical rigor. By completing this course, you'll master the foundational representations and algorithms that power recommendation engines, diagnostic systems, and causal inference applications across industries. By the end of this course, you will be able to:

Apply conditional independence principles to construct Bayesian and Markov network representations for a given real-world problem statement, Analyze variable-elimination and belief-propagation outputs to compute marginal probabilities and identify computational bottlenecks in small networks, and Evaluate the trade-offs between exact and sampling-based inference methods to recommend an approach suitable for a network's size and sparsity. This course is unique because it combines theoretical foundations with hands-on Python implementation using pgmpy and pomegranate, providing both mathematical understanding and practical coding experience. To be successful in this project, you should have a background in probability theory, basic graph theory, and Python programming.