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

4.6
stars
1,441 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

CC

Mar 24, 2020

really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it

CB

Jul 16, 2017

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

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126 - 150 of 314 Reviews for Probabilistic Graphical Models 1: Representation

By AlexanderV

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Mar 9, 2020

Great course, except that the programming assignments are in Matlab rather than Python

By Ning L

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Oct 17, 2016

This is a very good course for the foundation knowledge for AI related technologies.

By Hong F

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Jun 21, 2020

Hope there are explanations of the hard questions (marked by *) in the final exam.

By Abhishek K

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Nov 6, 2016

Difficult yet very good to understand even after knowing about ML for a long time.

By chen h

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Jan 20, 2018

The exercise is a little difficult. Need to revise several times to fully digest.

By Isaac A

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Mar 23, 2017

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

By 庭緯 任

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Jan 10, 2017

perfect lesson!! Although the course is hard, the professor teaches very well!!

By Alejandro D P

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Jun 29, 2018

This and its sequels, the most interesting Coursera courses I've taken so far.

By Naveen M N S

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Dec 13, 2016

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

By Amritesh T

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Nov 25, 2016

highly recommended if you wanna learn the basics of ML before getting into it.

By Pouya E

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Oct 13, 2019

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

By David C

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Nov 1, 2016

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

By Camilo G

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Feb 4, 2020

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

By PRABAL B D

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Sep 1, 2018

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

By Trọng T P

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Dec 13, 2019

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

By Lik M C

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Jan 12, 2019

A great course! The provided training clarifies all key concepts

By Sivaramakrishnan V

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Jan 6, 2017

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

By Arjun V

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Dec 3, 2016

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

By Lee C H

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Sep 5, 2024

an excellent course that lay my foundation of bayesian network

By CIST N

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Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

By pras v

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Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

By Gautam B

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Jul 4, 2017

Great course loved the ongoing feedback when doing the quizes.

By Achen

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May 6, 2018

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

By Albert J

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Nov 4, 2017

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

By Arthur C

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Jun 4, 2017

Super useful if you want to understand any probability model.