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 stars71,27 %
- 4 stars21,17 %
- 3 stars5,24 %
- 2 stars1,04 %
- 1 star1,25 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
Great course, except that the programming assignments are in Matlab rather than Python
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am
I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!
Acerca de Programa especializado: modelos gráficos de probabilidades
¿Cuándo podré acceder a las lecciones y tareas?
¿Qué recibiré si me suscribo a este Programa especializado?
¿Hay ayuda económica disponible?
Learning Outcomes: By the end of this course, you will be able to take a given PGM and
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