About this Specialization

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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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Advanced Level
Approximately 4 months to complete
Suggested pace of 11 hours/week
English
Shareable Certificate
Earn a Certificate upon completion
100% online courses
Start instantly and learn at your own schedule.
Flexible Schedule
Set and maintain flexible deadlines.
Advanced Level
Approximately 4 months to complete
Suggested pace of 11 hours/week
English

How the Specialization Works

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Hands-on Project

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There are 3 Courses in this Specialization

Course1

Course 1

Probabilistic Graphical Models 1: Representation

4.6
stars
1,405 ratings
Course2

Course 2

Probabilistic Graphical Models 2: Inference

4.6
stars
478 ratings
Course3

Course 3

Probabilistic Graphical Models 3: Learning

4.6
stars
297 ratings

Offered by

Placeholder

Stanford University

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