Retour à Probabilistic Graphical Models 1: Representation

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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....

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!!

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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par Alexandru I

•Nov 25, 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

par Rajmadhan E

•Aug 07, 2017

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

par Lucian B

•Jan 15, 2017

Some more exam questions and variation, including explanations when failing, would be very useful.

par BOnur b

•Nov 13, 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

par Elvis S

•Oct 29, 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

par Youwei Z

•May 20, 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

par Umais Z

•Aug 23, 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

par Hao G

•Nov 01, 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

par Stephen F

•Feb 26, 2017

This is a course for those interested in advancing probabilistic modeling and computation.

par liang c

•Nov 15, 2016

Great course. and it is really a good chance to study it well under Koller's instruction.

par Ning L

•Oct 18, 2016

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

par Abhishek K

•Nov 06, 2016

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

par chen h

•Jan 21, 2018

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

par Isaac A

•Mar 23, 2017

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

par 庭緯 任

•Jan 10, 2017

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

par Alejandro D P

•Jun 30, 2018

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

par Naveen M N S

•Dec 13, 2016

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

par Amritesh T

•Nov 25, 2016

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

par Pouya E

•Oct 13, 2019

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

par David C

•Nov 01, 2016

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

par PRABAL B D

•Sep 01, 2018

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

par Pham T T

•Dec 13, 2019

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

par Lik M C

•Jan 12, 2019

A great course! The provided training clarifies all key concepts

par SIVARAMAKRISHNAN V

•Jan 06, 2017

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

par Arjun V

•Dec 04, 2016

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