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
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- 3 stars5.38%
- 2 stars3.03%
- 1 star0.67%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
Excellent course! Everyone interested in PGM should consider!
A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.
Tougher course than the 2 preceding ones, but definitely worthwhile.
About the Probabilistic Graphical Models Specialization
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