Excellent course. The video lectures are challenging (had to keep my finger on the pause key) even if you're familiar with the math, since the instructor encapsulates concepts in an amazingly concise manner. This pays off with a lot of "Aha!" moments as strong concepts are combined to create insights, especially starting around week 3. I'm already in love with this subject after 1 part

It would have been nice to have more worked homework problems, since this is a math course. But, this is not necessary to pass the class or understand the concepts. I've purchased Prof Koller's text on PGM and hope to solidify some of the intuitions I'm missing shortly.

Taking off a star because the test cases and grading software for the honors homework assessments were clearly low effort and sometimes incorrect. There were a lot of cases where functions passed all the provided and automatic test cases despite major flaws (e.g. not working for any cases besides n=1), which made it difficult to tell if things worked since the programming style is unique. The homework itself was super interesting and valuable, but I probably spent over 50% of the time fighting the grader instead of learning stuff. Given that I'm a professional programmer and completed most of the homework in 25-50% of the estimated time, I'm guessing that the average student wasted even more time with issues that are ultimately unrelated to our understanding of PGM.

Before I took this course I took the Stanford Machine Learning course, which I greatly enjoyed. That course allows for the learning of difficult concepts in a way that I found less painful than working through a textbook. In this course there is a lot less video content, and the coding assignments are less interesting. Expect to spend a lot of time understanding the nuances of the code that the instructional team has developed, and be prepared to really pore over the gritty aspects of Octave or MATLAB. If you're serious about this course I suggest buying the accompanying book. The slides are not easy to understand without the audio narration, which makes them difficult to review, and unlike the case in the ML course, there are not a lot of readily available open introductions written on the topics.

I really enjoyed this course. Prof Koller presents the material very well, and it's really interesting to see how probabilistic graphical model frameworks are underpinned mathematically. I thought it was a pretty tough course at points, and while the lectures are good I found having a copy of Prof Koller's textbook very useful.

I would give this course 5 stars, but I thought some of the programming assignments involved too much grappling with MATLAB rather than illuminating the principles in the lectures. Also, I think the order of the lectures may have been changed since the course was first run as there are occasional references to things that have not been covered at that point.

Overall though, very enjoyable. I'm looking forward to parts 2 and 3.

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.

very nice intuition from the professor Daphne Koller and "compact" in these lectures that dont exceed 15min each. really glad i did the first one, wish i did also the other two parts, certainly will when i find the time. Just as a comment, i mostly enjoyed the programming assignments. they are very well structured and in a very particular manner, which at the same time is the strong and the weak point of the assingment, since at times i undertsood something else than what the actual implementation was. anyway they were really a challenge, and whoever manages to do them should be glad with his work. Thank you prof. Koller for this course!

The video lectures are really good and are useful for guiding you through Probabilistic Graphical Models book. I did not like the honor track programming exercises, however. The problems seems artificial to me and they make use of very strange data structures (probably due to the adoption of matlab as programming language). You end up wasting a lot of time with unimportant points instead of exploring ideas and getting cool results. Furthermore, I don't think the programming exercises help to familiarize the user to any of standard tools for bayesian analysis (i.e. probabilistic programming languages and so on).

The course helps me understand what a probabilistic graphical model is and how and why it works. One aspect I like the most about the course is the programming assignments. Those PA really make a lot of concepts clearer although sometimes you need to think carefully when the instruction is hard to follow. I think there should be more test case and expected results so that students know what is asked and to evaluate their own code. The instructor is generally clear but sometimes she goes too fast on certain concept. The course is hard but if you gives in time and effort you can complete it.

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.

I have learn a lot throughout the course. The content is quite niche. It is hard find good resources for the content else where.

That said there were a couple of issues.

- The submit_configuration file needs to be modified due to programming errors for MATLAB

- Programming assignments should be given in Python not MATLAB

- To make the assignments more clear it would be good if you had video on how to test the code students submit before submitting. Week 3 is quite confusing.

The content is good. I'm excited to learn enough about these techniques to use them in my projects at work. The quizzes seem overly complicated and have trickily worded questions, especially for the honors parts. The programming assignments are tough, which is OK, but the bugs in the submission process make completing them very frustrating. The forums are full of people trying to reach admins / mentors to get things fixed without success.

This course is a very essential learning step for people who want to learn and work with Baysean or Markov networks. I think that the course can be further improved by going a little slow on certain assertions or deductions which are fundamental to the subject. Those should be properly emphasized. But overall the assignments were challenging and actually made you think about the things taught in that corresponding video.

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!

This is overall a great course. It required me a bit of reading outside of the course material, and fail on quizzes a few times before understanding, but it is was very much worth the effort. However, the assignments in MATLAB and IAMSAM feel dated. As much as I would like to exercise the newly acquired knowledge with exercises MATLAB is particularly irritating after having used other languages.

The course Information tell us that we can buy Professor Koller's book with a discount from Amazon. I tried but amazon didn't like the code. Is this information still true.

I regularly wanted to go and read a book to understand what more of what I was being told but at £70 a pop this is a challenging purchase

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....

This course captures very advanced concepts about probabilistic inference. I found the lectures are more interesting but I wish some explainations were more elaborative, as some concepts are hard to grasp. I had to go through some videos again and again. The forums were helpful as well.

It is a magnificent course, terrific information and lectures. Nonetheless, please update your programing exercises . Consider utilising either Matlab, R, Python or GenIE. SamIAM is terrible for the installation and ,as in my experience, it simply did not work.

Regards

Abraham

Some of the examples are a bit confusing. I mostly used logic to solve these versus following a formula. Octave was fine but I didn't know how to use SAMIAM and so gave up on the coding assignments since PGMs aren't a focus area for me except for general theoretical knowledge.

Good course. Sometimes a little bit hard to follow. For example representation of probability functions as graphs (connection between factorisation of probabilty distribution and cliques in the graph). And I'm not sure If I can apply PGMs to real world problems now.

In general this is a good introductory course. You should read the book if you want more in-depth knowledge in this field. I feel that some of the concepts can be expanded a little more, like local structure in Markov model. Overall, this is a great course.

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.

The materials are very interesting, however, this professor speaks so fast that it is hard to grasp the deep theory. In overall, this course is great. And I really need to do the assignment to enhance my comprehension about the content.

Wonderful course with great material. Wish there were more examples in the material. Nonetheless cannot complain to get this course for free with SEE material and programming assignments (need to complete yet in this session)

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.

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.