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
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Advanced Level
Approx. 4 months to complete
Suggested 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
Approx. 4 months to complete
Suggested 11 hours/week
English

There are 3 Courses in this Specialization

Course1

Course 1

Probabilistic Graphical Models 1: Representation

4.7
stars
1,297 ratings
287 reviews
Course2

Course 2

Probabilistic Graphical Models 2: Inference

4.6
stars
448 ratings
68 reviews
Course3

Course 3

Probabilistic Graphical Models 3: Learning

4.6
stars
278 ratings
46 reviews

Offered by

Placeholder

Stanford University

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