Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art 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 foundational tool in formulating many machine learning problems.
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TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.
About the Probabilistic Graphical Models Specialization
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Learning Outcomes: By the end of this course, you will be able to
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