Chevron Left
Back to Probabilistic Graphical Models 1: Representation

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

4.6
stars
1,400 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

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:

176 - 200 of 304 Reviews for Probabilistic Graphical Models 1: Representation

By Labmem

Oct 3, 2016

By phung h x

Oct 30, 2016

By Frédéric L M

Nov 19, 2017

By Diego T

Jun 9, 2017

By Yue S

May 9, 2019

By David D

May 30, 2017

By Yang P

Apr 26, 2017

By Nairouz M

Feb 13, 2017

By brotherzhao

Feb 15, 2020

By Utkarsh A

Dec 30, 2018

By Musalula S

Aug 2, 2018

By Yuri F

May 15, 2017

By 赵紫川

Nov 27, 2016

By Pedro R

Nov 9, 2016

By Frank

Dec 14, 2017

By HOLLY W

May 24, 2019

By Siyeong L

Jan 21, 2017

By Alireza N

Jan 12, 2017

By dingjingtao

Jan 7, 2017

By Phan T B

Dec 2, 2016

By Jax

Jan 8, 2017

By Jose A A S

Nov 25, 2016

By mohammed o

Oct 18, 2016

By zhou

Oct 13, 2016

By 张浩悦

Nov 22, 2018