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.
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Skills you will gain
- 5 stars71.38%
- 4 stars19.52%
- 3 stars5.38%
- 2 stars3.03%
- 1 star0.67%
TOP REVIEWS FROM PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
Tougher course than the 2 preceding ones, but definitely worthwhile.
Awesome course... builds intuitive thinking for developing intelligent algorithms...
very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.
Plz give practical assignments in Python. Matlab is not free and not many and neither myself know Matlab.
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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