Back to Probabilistic Graphical Models 1: Representation

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246 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 Luiz C

•Jun 26, 2018

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

By Michael K

•Nov 14, 2016

This excellent course is exceptional in that very few MOOCs are taught at this graduate level. Others have pointed out that while this is an introductory course to Probability Graphical Models, I would say that this is still an advanced course, with lots of prerequisites. Prof. Koller is an excellent lecturer, yet moves fast, and you'll need to do reading to fill in the gaps. I haven't been able to find a good book to accompany the course, as her book is pretty dry. I strongly recommend one complete all of the Honors assignments to get a lot out of the course. The discussion boards are not so active with plenty of unanswered questions. Doing the programming assignments will greatly enhance your skills in debugging.

By Mehmet U

•Jul 02, 2017

Thanks for offering this course, I have learned a lot. However, the course is quite confusing. Not everything is well defined so it is hard to answer some questions. The honors programming assignments are usually confusing in this manner. If you put in the effort to understand it thou, it can be done. To be honest thou, some misunderstanding could be given to my lack of understanding the material at first. At the same time my lack of understanding is probably caused by the course material being not so well defined. Maybe it would help if one spends more time reading the text book.

By Rick d W

•Apr 20, 2017

Everything is explained very clearly throughout the course, and the structure they use to teach the subject , from basics to advanced material, is especially helpful. Would recommend this course to anyone with an interest in probabilistic modelling.

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 mathieu.zaradzki@gmail.com

•Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

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 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 Ahmad E

•Aug 20, 2017

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

By Zhen L

•Nov 16, 2016

The course gives an good introduction of PGM. The highlights are the well-designed quizzes and assignments. But the videos of lectures are not good enough. It's too fast and some key concepts are not clearly explained.

After looked into another course on coursera, I add a star for this....

By Alberto C

•Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

By Caio A M M

•Dec 03, 2016

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

By 李俊宏

•Nov 09, 2017

This is a tough course so it was split into 3 parts. I've learned some ideas about bayesian network and markov model. The major problem about this course is the programming assignment, which is poorly maintained. Daphne Koller is very brilliant but this makes it hard for people to catch up with her, especially for people whose mother language is not English. After all, this is an interesting course!

By Andres P N

•Jun 27, 2018

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

By Soteris S

•Nov 27, 2017

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

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 tyang16

•Jun 20, 2019

too hard

By Soumyadipta D

•Jul 16, 2019

lectures are too fast otherwise great

By Anshuman S

•May 08, 2019

I would recommend adding some supplemental reading material.

By Michael B

•Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

By Michel S

•Jul 14, 2018

Good course, but the material really needs a refresh!

By Nicholas E

•Oct 29, 2016

The course was very interesting and thought-provoking. I found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism. The discussions highlighted exciting aspects of their power in simplifying complex problems involving uncertainty. However, I still do not feel I could propose convincing PGMs for real-world problems. There are examples in the course, but they are far removed from being concrete applications. I would have preferred there be an in depth analysis of an application of PGMs in the literature over the lengthy programming assignments. I am an experienced programmer with over 5 years of experience in many languages including MATLAB/Octave and I sometimes found it uninspiring to solve toy problems, not due to the difficulty in using the programming language, but rather because after the assignment had been completed I felt I had not really learnt much more than I would have from just watching the lectures, although, if you are interested in getting experience with MATLAB/Octave, the programming assignments are good practice. I qualify this in stating that I have not yet completed the next two courses on PGMs; this course may present an essential foundation that is necessary for the upcoming courses, and in any case provoked my interest in learning more about them

By Mahendra K

•Oct 04, 2017

The course is highly theoretical. Would have been great if it was paced well and driven from real world examples. I am not saying that there are no examples. But it'd have been better if the concepts were driven via some real world examples instead of first talking about the concept and then its applications.

What would have been even better if Python was an option for PAs. Octave can't be used in industry setting where the amount of data is really large. Both Python and Octave should have been an option so that the student can decide for themself.

By Roman G

•Nov 04, 2016

The audio is VERY VERY poor.

That makes it very hard to understand what Prof Kohler is trying to impart on us..

I often lost track

By Siavash R

•Aug 11, 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

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