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 você terá
- 5 stars74,76%
- 4 stars17,74%
- 3 stars5,20%
- 2 stars0,99%
- 1 star1,28%
Principais avaliações do 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
Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.
really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it
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
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