The virtual course "Probabilistic graphic models 2: Inference - Virtual Course - Coursera", is a course with different contents and offers video classes of Approx. 38 hours to complete. Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
Probabilistic graphical models (PGMs) are a rich framework for coding probability distributions over complex domains: joint (multivariate) distributions over a large number of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, drawing on concepts from probability theory, graphing algorithms, machine learning, and more. They are the foundation of the most advanced methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a fundamental tool in the formulation of many machine learning problems. This course is the second in a sequence of three. After the first course, which focused on representation, this course addresses the question of probabilistic inference - how a PGM can be used to answer questions. Although a PGM typically describes a very high dimension distribution, its structure is designed to allow questions to be answered efficiently. The course introduces both exact and approximate algorithms for different types of inference tasks and discusses where each might best be applied. The honors track (highly recommended) contains two hands-on programming assignments, in which the key routines of the most widely used exact and approximate algorithms are implemented and applied to a real-world problem. and discusses where each could best be applied. The honors track (highly recommended) contains two hands-on programming assignments, in which the key routines of the most widely used exact and approximate algorithms are implemented and applied to a real-world problem. and discusses where each could best be applied. The honors track (highly recommended) contains two hands-on programming assignments, in which the key routines of the most widely used exact and approximate algorithms are implemented and applied to a real-world problem.
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