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 stars74,76 %
- 4 stars17,74 %
- 3 stars5,20 %
- 2 stars0,99 %
- 1 star1,28 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos
The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.
learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.
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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