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Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University

4.6
stars
1,442 ratings

About the Course

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. This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....

Top reviews

AS

Sep 8, 2023

Everything is fine except the bugs in programming assignments. Although it says advance course, the programming assignments aren't that hard. The problems is difficult to submit it to Coursera.

AM

Nov 3, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

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176 - 200 of 314 Reviews for Probabilistic Graphical Models 1: Representation

By Miriam F

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Aug 28, 2017

Very nice and well prepared course!

By Gary H

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Mar 28, 2018

Great instructor and information.

By Subham S

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Apr 29, 2020

I enjoyed the course very much!

By Georgios S

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Jun 18, 2017

Excellent material presentation

By 郭玮

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Apr 26, 2019

Really nice course, thank you!

By 지혜성

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Oct 10, 2019

So difficult. But interesting

By Jinsun P

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Jan 17, 2017

Really Helpful for Studying!

By Shengding H

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Mar 10, 2019

A very nice-designed course

By Marno B

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Feb 3, 2019

Absolutely love it!!!!

:)

By An N

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Feb 6, 2018

Thank you, the professor.

By hy395

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Sep 13, 2017

Very clear and intuitive.

By 艾萨克

•

Nov 7, 2016

useful！ A little diffcult

By Souvik C

•

Oct 26, 2016

Extremely helpful course

By Joris S

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Feb 16, 2020

Well presented course!

By Jessie W

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Apr 17, 2018

very good, practical.

By Wei C

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Mar 7, 2018

good online coursera

By Nijesh U

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Jul 19, 2019

Thanks for offering

By Hang D

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Oct 9, 2016

really well taught

By Anil K

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Oct 30, 2017

Very intuitive...

By Kar T Q

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Mar 2, 2017

Excellent course.

By Labmem

•

Oct 3, 2016

Great Course!!!!!

By phung h x

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Oct 31, 2016

very good course

By Frédéric L M

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Nov 19, 2017

Great course !

By Diego T

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Jun 10, 2017

Great content!

By Yue S

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May 9, 2019

Great course!