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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- 5 stars71,21%
- 4 stars21,21%
- 3 stars5,25%
- 2 stars1,05%
- 1 star1,26%
Principais avaliações do PROBABILISTIC GRAPHICAL MODELS 2: INFERENCE
Great course! Course has filled gaps in my knowledge from statistics and similar sciences.
It would be great to have more examples included in the lectures and slides.
Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am
Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.
Sobre Programa de cursos integrados Modelos Gráficos Probabilísticos
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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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