Back to Probabilistic Graphical Models 1: Representation
Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University
1,439 ratings
About the Course
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 proble...
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 proble...
Top reviews
RG
Jul 12, 2017
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
CM
Oct 22, 2017
The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).
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