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

1,150 ratings
250 reviews

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


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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1 - 25 of 244 Reviews for Probabilistic Graphical Models 1: Representation

By Sandeep M

Sep 23, 2018

The content of the course is good but the assignments are in matlab which isn't as widely used as python and has the additional headache of licensing. it is the assignments where you really learn things so this is a serious negative point.

By Ben L

Jan 13, 2019

Would be better if there are people monitoring the discussion board and actually answer student's questions.

By Amine M

Apr 30, 2019

The material is really important and helpful for many concepts of Machine Learning. Daphne Koller is very good at explaining complicated ideas in an intuitive way. The programming assignments are very relevant and cover many real-world application scenarios in medical diagnosis and testing. Unfortunately, programming assignments have many flaws. First, some scripts do not work and therefore it is necessary to manually adjust these in order to submit your assignment part by part. Second, the forum is almost dead, which means that is is difficult to get help once you are stuck at a problem. Most of the helpful posts are almost two years old. Third, often times questions in the quiz are very vague and not clearly formed which makes it difficult to answer the instructor's question. All in all, I think, that the course is worthwhile but nonetheless the course definitely needs some refurbishing and bugs in scripts need to be fixed.

By M

Jan 06, 2018

Good course, with actual university level content and depth (albeit in a multiple choice format). The explanations of the material were clear, however if you don't have at least a surface level familiarity with Bayesian probability and first year university level math, you'll find yourself spending a lot of time looking up random jargon on Wikipedia.

If you lack the necessary background, I suggest reviewing the content of Stanford CS109 (the content is publically available).

The assignments were a bit opaque / wordy; instead if an essay, provide clear bullet point tasks with a detailed appendix for clarity. Also, please use Python instead of Matlab. It's free, there's a more support available for it, it has much clearner syntax, much more comprehensive libraries and it's at least tollerably performant (in comparison to Matlab / Octave).

By Alexander P

Apr 02, 2019

I really enjoyed the content of this course. Having been inspired by reading The Book of Why, I was looking for some formal language around Bayesian Networks and this course really fit the bill. My biggest piece of feedback is on the programming assignments. These really should be in Python. Octave is an okay choice, and I suspect might have to do with Andrew Ng original choice to use it for his own machine learning course. However, the data science community writ large uses Python and R, which is why Andrew switched to Python for his deep learning courses. I would recommend the programming assignment be updated so that they are more accessible to the data science community.

By Michael S E

Feb 14, 2017

This course was solid overall but not excellent. I learned the basics of different classes of probabilistic models including Bayesian networks and Markov networks and how to represent them. Prof. Koller is knowledgeable and presented the materially logically. With that said, this course could have been a lot better than it was.

The honors programming assignments could have been excellent The material was interesting and dovetailed well with the course content. But the assessment process was very frustrating and led to a lot of wasted time debugging that was geared more to quirks of the grader than to course concepts. Both test cases and feedback on failed submissions were woefully inadequate. Some of the quizzes were also frustrating, featuring what I consider to be "gotcha" questions geared more to creating a grading curve than to measuring understanding of the material.

Advice to course staff: (1) Please provide more test cases on coding assignments (2) Please provide better feedback in submission reports (3) Please monitor the discussion boards more actively for unanswered questions (4) If you want to provide an externally linked executable you intend students to run from Matlab, it's not reasonable to give a 32 bit file in 2017 and send us down a rabbit hole where you suggest we build the executable from source, which in turn requires us to build the boost library from source.

By Deleted A

Nov 18, 2018

This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.

By StudyExchange

Mar 13, 2018

In the video, a lot of knowledge point do not explain very clearly, we do not konw how to resolve the quizzes. Moreover, if buy the textbook, may acquire more detail about PGM, but the textbook do not explain very clear neither. Textbook is hard to read. Even so, this course is worthwile to learn. Because PGM is one of the basic theory of machine learning and widespread use. In the end, thank Koller and coursera! Thank you very much!

By Shi Y

Nov 13, 2018


By Dhruv P

Jun 18, 2017

I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.

Thank You Daphne Mam. God Bless Everybody Associated with it.

By Santosh K S

Jul 28, 2018

Dear Madam thanks a lot for the course.

This course - in addition to Machine Learning, by Andrew Ng Sir, are perhaps most comprehensive courses.

This course covers a lot over a period of 5 weeks. It demands higher level of focus. So, the learning still continues..


Santosh Kumar Singh

Bangalore, India

By Alex L

Apr 09, 2018

This is not an easy course, so beware. The instruction is solid but you still need to reason through a lot on your own, and especially if you choose to complete the Honors programming section (which I highly recommend to prove to yourself that you really understand what you have learned and can apply it), you really need to plan on allocating sufficient number of hours to work through the programming assignments. You'll likely need to re-watch several of the video segments several times for it to really sink in, as well as referencing the Discussion Forum when you are stuck and need inspiration. Once you do complete this course (after many hours of work and thought) you will enjoy a deep sense of accomplishment, will look and think about decision-making in a fresh new way, and have learned many very useful skills.

By Phillip W

Apr 08, 2019

Sometimes the questions weren't clear. But in general, I really like the course and the things I've learnt I am sure they are useful.

By Tomasz L

May 12, 2019

Great course! Lectures are clear and comprehensive. Quizzes really check knowledge and are challenging. In the programming assignments the main focus is put on implementation of PGM algorithms and not on technical aspects of Octave/Matlab. Some changes could be made in Programing Assignment 4 to make description and provided code easier to understand.

By Lorenzo B

Jan 19, 2019

The course contents are presented very clearly. Difficult ideas are conveyed in a precise and convincing way. Despite this, the global structure is not presented very clearly, and the quality of some course material is not excellent. In particular, I didn't find the optional programming assignments particularly interesting, and the code/questions contained more than one bug. Also, the quality of video/sound is quite poor, and varies a lot from course to course.

By Utkarsh A

Dec 30, 2018

maza aa gaya

By 张浩悦

Nov 22, 2018


By Alexandru I

Nov 25, 2018

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

By BOnur b

Nov 13, 2018

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

By Mahmoud S

Feb 25, 2019

Very good explanation and excellent assignments

By Shengding H

Mar 10, 2019

A very nice-designed course

By 刘仕琪

Oct 31, 2016


By Musalula S

Aug 02, 2018

Great course

By Pablo G M D

Jul 18, 2018

Outstanding teaching and the assignments are quite useful!


Sep 01, 2018

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