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 stars71,21 %
- 4 stars21,21 %
- 3 stars5,25 %
- 2 stars1,05 %
- 1 star1,26 %
Principales reseñas sobre PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts
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.
Great course! Course has filled gaps in my knowledge from statistics and similar sciences.
Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.
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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