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

4.6
stars
1,432 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

RG

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 314 Reviews for Probabilistic Graphical Models 1: Representation

By Ruiliang L

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Feb 15, 2021

Awesome class to gain solid understanding of graphical model

By Phong V

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Mar 18, 2020

Great course, learned a lots. Thanks professor Daphne Koller

By Sriram P

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Jun 24, 2017

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

By Pablo G M D

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Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

By Henry H

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Nov 14, 2016

Very informative course, and incredibly useful in research

By Ingyo J

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Oct 4, 2018

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

By Albedo

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Oct 29, 2022

Very good course. Thanks for ability to learn this.

By EPerishko

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Jul 24, 2023

Nice and intensive lectures, very well structured.

By Renjith K A

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Sep 23, 2018

Was really helpful in understanding graphic models

By Roger T

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Mar 5, 2017

very challenging class but very rewarding as well!

By Harshit A

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Apr 20, 2021

This is a challenging but very satisfying course.

By 吕野

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Dec 26, 2016

Good course lectures and programming assignments

By Mahmoud S

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Feb 25, 2019

Very good explanation and excellent assignments

By Lilli B

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Feb 2, 2018

Brilliant content and charismatic lecturer!!!

By Fabio S

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Sep 25, 2017

Excellent, well structured, clear and concise

By Orlando D

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Jul 19, 2017

Very good and excellent course and assignment

By Parag S

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Aug 14, 2019

Learn the basic things in probability theory

By Christian S

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Dec 11, 2020

Highest level in coursera courses so far.

By Jonathan H

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Nov 25, 2017

This course is hard and very interesting!

By Shengliang X

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May 29, 2017

excellent explanations! Thanks professor!

By Alexander K

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May 16, 2017

Thank you for all. This is gift for us.

By Chahat C

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May 4, 2019

lectures not good(i mean not detailed)

By Harshdeep S

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Jul 19, 2019

Excellent blend of maths & intuition.

By NARENDRAN

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Mar 7, 2020

Very good explanation on the subject

By Jui-wen L

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Jun 20, 2019

Easy to follow and very informative.