Back to Probabilistic Graphical Models 1: Representation

4.7

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1,199 ratings

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259 reviews

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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By Jack A

•Nov 05, 2017

The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

By Gorazd H R

•Jul 07, 2018

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

By Ashwin P

•Jan 09, 2017

Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

By Forest R

•Feb 20, 2018

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

By Xiaojie Z

•Dec 22, 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

By Luiz C

•Jun 26, 2018

Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory

By Werner N

•Dec 28, 2016

Very good course. It should contain more practical examples to make the material better to understand.

By Haitham S

•Nov 24, 2016

Great course, however, the honors track assignments are a bit too tedious and take lots of time.

By Kevin W

•Jan 17, 2017

The course is pretty good. I love the way that the professor led us into the graphical models.

By Péter D

•Oct 29, 2017

great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

By Andres P N

•Jun 27, 2018

There are many error in the implementations for octave. Aside from that, the course is fine

By Ahmad E

•Aug 20, 2017

Covers some material a little too quickly, but overall a good and entertaining course.

By Soteris S

•Nov 27, 2017

A bit more challenging than I thought but very useful, and very well structured

By mathieu.zaradzki@gmail.com

•Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

By Caio A M M

•Dec 03, 2016

Instructor is engaging in her delivery. Topic is interesting but difficult.

By Michael B

•Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

By Anshuman S

•May 08, 2019

I would recommend adding some supplemental reading material.

By Jhonatan d S O

•May 25, 2017

Rich content and useful tools for applying in real problems

By Alberto C

•Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

By Yuanduo H

•Jan 20, 2020

Five stars minus the week 4 coding homework

By Arthur B

•Jan 08, 2017

More feedback from TA would be appreciated

By Myoungsu C

•Dec 26, 2018

Writing on the ppt is not clear to see.

By Soumyadipta D

•Jul 16, 2019

lectures are too fast otherwise great

By Sunsik K

•Jul 31, 2018

Broad introduction to general issues

By Tianyi X

•Feb 20, 2018

Lack of top-down review of the PGM.

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