Course Description

In this advanced course offered by Stanford University, students will delve into the fascinating world of Probabilistic Graphical Models (PGMs). PGMs are powerful tools for representing and reasoning about complex systems that involve uncertainty. This course will provide students with the skills and knowledge needed to effectively use PGMs in real-world applications. Throughout the course, students will learn about various aspects of PGMs, including Bayesian networks, probability and statistics, probability distributions, general statistics, graph theory, Bayesian statistics, correlation and dependence, Markov models, network models, and decision making. These skills are essential for anyone looking to work with PGMs in fields such as data science, machine learning, and artificial intelligence. With a rating of 4.6 stars and over 4,000 reviews, this course has been highly praised by students for its comprehensive and engaging content. The course is designed for advanced learners and is expected to take between 1 to 3 months to complete, depending on