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

4.6
stars
1,346 ratings
•
300 reviews

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

ST
Jul 12, 2017

Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!

CM
Oct 22, 2017

The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).

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151 - 175 of 293 Reviews for Probabilistic Graphical Models 1: Representation

By Gary H

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Mar 27, 2018

Great instructor and information.

By Subham S

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Apr 28, 2020

I enjoyed the course very much!

By George S

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Jun 18, 2017

Excellent material presentation

By 郭玮

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Apr 25, 2019

Really nice course, thank you!

By hyesung J

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Oct 10, 2019

So difficult. But interesting

By Jinsun P

•
Jan 16, 2017

Really Helpful for Studying!

By Shengding H

•
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 5, 2018

Thank you, the professor.

By hy395

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Sep 13, 2017

Very clear and intuitive.

By 艾萨克

•
Nov 6, 2016

useful！ A little diffcult

By Souvik C

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Oct 26, 2016

Extremely helpful course

By Joris S

•
Feb 16, 2020

Well presented course!

By Jiew W

•
Apr 17, 2018

very good, practical.

By Wei C

•
Mar 6, 2018

good online coursera

By Nijesh U

•
Jul 18, 2019

Thanks for offering

By Hang D

•
Oct 9, 2016

really well taught

By Anil K

•
Oct 30, 2017

Very intuitive...

By Kar T Q

•
Mar 2, 2017

Excellent course.

By Labmem

•
Oct 3, 2016

Great Course!!!!!

By phung h x

•
Oct 30, 2016

very good course

By Logé F

•
Nov 19, 2017

Great course !

By Diego T

•
Jun 9, 2017

Great content!

By Yue S

•
May 9, 2019

Great course!

By David D

•
May 30, 2017

Mind blowing!

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