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.
About this Course
Skills you will gain
- 5 stars74.75%
- 4 stars17.76%
- 3 stars5.20%
- 2 stars0.99%
- 1 star1.28%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos
Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it
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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