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 stars71.27%
- 4 stars21.17%
- 3 stars5.24%
- 2 stars1.04%
- 1 star1.25%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
It would be great to have more examples included in the lectures and slides.
I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.
About the Probabilistic Graphical Models Specialization
Frequently Asked Questions
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Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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