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.
Learner Career Outcomes
43%
Started a new career after completing this specialization.
17%
Got a pay increase or promotion.
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
Learner Career Outcomes
43%
Started a new career after completing this specialization.
17%
Got a pay increase or promotion.
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

Take Courses

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Hands-on Project

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Earn a Certificate

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There are 3 Courses in this Specialization

Course1

Course 1

Probabilistic Graphical Models 1: Representation

4.6
stars
1,333 ratings
298 reviews
Course2

Course 2

Probabilistic Graphical Models 2: Inference

4.6
stars
463 ratings
73 reviews
Course3

Course 3

Probabilistic Graphical Models 3: Learning

4.6
stars
289 ratings
50 reviews

Offered by

Placeholder

Stanford University

Frequently Asked Questions

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