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

4.7
stars
1,214 ratings
265 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 13, 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 23, 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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101 - 125 of 258 Reviews for Probabilistic Graphical Models 1: Representation

By Pouya E

Oct 13, 2019

Well-structured content, engaging programming assignments in honors track.

By David C

Nov 01, 2016

If you are interested in graphical models, you should take this course.

By Camilo G

Feb 05, 2020

Professor Koller does an amazing job, I fully recommend this course

By PRABAL B D

Sep 01, 2018

Awesome Course. I got to learn a lot of useful concepts. Thank You.

By Pham T T

Dec 13, 2019

Excellent course! This course helps me so much studying about PGM!

By Lik M C

Jan 12, 2019

A great course! The provided training clarifies all key concepts

By SIVARAMAKRISHNAN V

Jan 06, 2017

Great course. Thanks Daphne Koller, this is really motivating :)

By Arjun V

Dec 04, 2016

A great course, a must for those in the machine learning domain.

By CIST N

Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

By Prazzy S

Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

By Gautam B

Jul 04, 2017

Great course loved the ongoing feedback when doing the quizes.

By Achen

May 06, 2018

a bit too hard if you don't have enough probability knowledge

By albert b

Nov 04, 2017

Best course anywhere on this topic. Plus Daphne is the best !

By Arthur C

Jun 04, 2017

Super useful if you want to understand any probability model.

By Vu P

Mar 18, 2020

Great course, learned a lots. Thanks professor Daphne Koller

By Sriram P

Jun 24, 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.

By Pablo G M D

Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

By Ziheng

Nov 14, 2016

Very informative course, and incredibly useful in research

By Ingyo C

Oct 04, 2018

What a wonderful course that I haven't ever taken before.

By Renjith K A

Sep 23, 2018

Was really helpful in understanding graphic models

By Roger T

Mar 05, 2017

very challenging class but very rewarding as well!

By 吕野

Dec 26, 2016

Good course lectures and programming assignments

By Mahmoud S

Feb 25, 2019

Very good explanation and excellent assignments

By Lilli B

Feb 02, 2018

Brilliant content and charismatic lecturer!!!

By Fabio S

Sep 25, 2017

Excellent, well structured, clear and concise