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 stars74,73 %
- 4 stars17,77 %
- 3 stars5,21 %
- 2 stars0,99 %
- 1 star1,28 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.
Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines
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.
I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.
Thank You Daphne Mam. God Bless Everybody Associated with it.
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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