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Probabilistic Graphical Models Specialization

Probabilistic Graphical Models. Master a new way of reasoning and learning in complex domains
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.

Applied Learning Project

Through various lectures, quizzes, programming assignments and exams, learners in this specialization will practice and master the fundamentals of probabilistic graphical models. This specialization has three five-week courses for a total of fifteen weeks.

Course Information

Estimated Time: Approximately 4 months to complete Suggested pace of 11 hours/week

Difficulty: Advanced

Free

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Course Information

Estimated Time: Approximately 4 months to complete Suggested pace of 11 hours/week

Difficulty: Advanced