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
Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.
Awesome class to gain better understanding of inference for graphical model
Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.
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.
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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