Back to Probabilistic Graphical Models 1: Representation

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265 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 Akshaya T

•Jan 16, 2018

Some tutorials need disambiguating documentation (upgrade :)) but otherwise, the course is really good. It would also help if there is a mention of what chapters to study from the book for every lesson -- in the slides.

By Rajeev B

•Dec 23, 2017

This specialization covers a lot of concepts and programming assignments which are very helpful in understanding the concepts clearly. Although, I wish there is some form of explanation for the programming assignments.

By Alain M

•Nov 03, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

By Boxiao M

•Jun 28, 2017

The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

By Shawn C

•Nov 05, 2016

The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.

By Hilmi E

•Feb 16, 2020

I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..

By Roman S

•Mar 20, 2018

A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.

By serge s

•Oct 18, 2016

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

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 François L

•Mar 16, 2020

Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)

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 Saurabh N

•Mar 24, 2020

The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

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.

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