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.
Об этом курсе
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- 4 stars17,78 %
- 3 stars5,21 %
- 2 stars1 %
- 1 star1,28 %
Лучшие отзывы о курсе PROBABILISTIC GRAPHICAL MODELS 1: REPRESENTATION
Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.
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.
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..
Специализация Графические вероятностные модели : общие сведения
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Learning Outcomes: By the end of this course, you will be able to
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