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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Habilidades que obtendrás
- 5 stars71,33 %
- 4 stars21,12 %
- 3 stars5,23 %
- 2 stars1,04 %
- 1 star1,25 %
Principales reseñas sobre 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 course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally
Very great course! A lot of things have been learnt. The lectures, quiz and assignments clear up all key concepts. Especially, assignments are wonderful!
Great course! Course has filled gaps in my knowledge from statistics and similar sciences.
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 take a given PGM and
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