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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- 5 stars71,38 %
- 4 stars19,52 %
- 3 stars5,38 %
- 2 stars3,03 %
- 1 star0,67 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 3: LEARNING
Great course, especially the programming assignments. Textbook is pretty much necessary for some quizzes, definitely for the final one.
Awesome course... builds intuitive thinking for developing intelligent algorithms...
Great course, though with the progress of ML/DL, content seems a touch outdated. Would
Excellent course! Everyone interested in PGM should consider!
Acerca de Programa especializado: modelos gráficos de probabilidades
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Learning Outcomes: By the end of this course, you will be able to
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