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
Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course
Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
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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