Chevron Left
Back to Probabilistic Graphical Models 1: Representation

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

1,400 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


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!!


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:

101 - 125 of 304 Reviews for Probabilistic Graphical Models 1: Representation

By Youwei Z

May 19, 2018

By Umais Z

Aug 23, 2018

By Hao G

Nov 1, 2016

By Alfred D

Jul 2, 2020

By Stephen F

Feb 26, 2017

By Una S

Jul 24, 2020

By liang c

Nov 15, 2016

By AlexanderV

Mar 9, 2020

By Ning L

Oct 17, 2016

By Hong F

Jun 21, 2020

By Abhishek K

Nov 6, 2016

By chen h

Jan 20, 2018

By Isaac A

Mar 23, 2017

By 庭緯 任

Jan 10, 2017

By Alejandro D P

Jun 29, 2018

By Naveen M N S

Dec 13, 2016

By Amritesh T

Nov 25, 2016

By Pouya E

Oct 13, 2019

By David C

Nov 1, 2016

By Camilo G

Feb 4, 2020


Sep 1, 2018

By Pham T T

Dec 13, 2019

By Lik M C

Jan 12, 2019

By Sivaramakrishnan V

Jan 6, 2017

By Arjun V

Dec 3, 2016