Chevron Left
Back to Probabilistic Graphical Models 1: Representation

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

4.6
stars
1,385 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

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).

Filter by:

51 - 75 of 301 Reviews for Probabilistic Graphical Models 1: Representation

By Douglas G

•

Oct 24, 2016

By Venkateshwaralu S

•

Oct 25, 2016

By Ryan D

•

Jun 21, 2017

By Jorge P

•

Feb 26, 2017

By Juan P J A

•

May 11, 2022

By roi s

•

Oct 29, 2017

By Anurag P

•

Jan 8, 2018

By Yuxuan X

•

Aug 7, 2017

By Minh N

•

Mar 1, 2017

By Christophe K

•

Oct 22, 2016

By Maxim V

•

Apr 29, 2020

By José A R

•

Sep 13, 2018

By Chatard J

•

Nov 25, 2016

By Justin C

•

Oct 23, 2016

By KE Z

•

Nov 23, 2017

By Alexey K

•

Nov 17, 2017

By Supakorn S

•

Apr 27, 2022

By Ofelia P R P

•

Dec 11, 2017

By Jorge C

•

Sep 17, 2017

By Christopher M P

•

Jan 16, 2020

By Christopher B

•

Jul 17, 2017

By Anthony L

•

Jul 20, 2019

By ChrisLJ

•

Mar 25, 2020

By Prasid S

•

Dec 7, 2016

By Al F

•

Mar 19, 2018

Coursera Footer

Start or advance your career

Browse popular topics

Popular courses and articles

Earn a degree or certificate online

Coursera

Community

More

Learn Anywhere
Placeholder
Placeholder
Placeholder