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.

Lectures very good, but the code in the programming assignments is awful.

Having done the first few programming assignments, I decided to switch to recode and do the programming excercises in python/numpy/scipy etc.

The code definitely should get an update, especially because for instance tensorflow starts to integrate tensorflow probability.

Video lectures were clear and the course content was detailed and explained clearly. I take one star off because some of the material needed for the quizzes wasn't in the main course material; for example, the sum-product algorithm was mentoned in one of the quiz questions, but wasn't mentioned in the main material.

Great course. Explained in a straightforward manner.

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.

The course content is really interesting and Daphne Koller is a fabulous presenter. Unfortunately, though, you are doing this course on your own - looks like there have been no TAs online for over 3 years, and if you're looking for support or assistance understanding any of the work you may find confusing or difficult then don't expect to get it here. Very disappointed that a paid course has virtually no support in it whatsoever.

Information is well presented. Tests are 4 questions. Any mistake in the answer counts as wrong, and all questions must be correct to receive the passing 80%. The course makes you wait an hour to retake the exam, so it is NOT friendly for folks on a time schedule.

Great topic, the professor is a top expert in the field, but the grading interface badly needs an upgrade. It is not acceptable for students to have to manually hack JSON submissions just to get around grader errors.

The support by the mentors could be much better. Because of the missing support I was not able to solve the assignments under Windows with Octave. I had to buy Matlab. (-2)

It seems to me that the course is very difficult to complete without additional sources. (-1)

Did not like how the concepts were introduced, it felt like learning theory for the sake of theory.

The course content is solid. The honours content is challenging and interesting. There's a couple of minor glitches that cause frustration in the PA's but nothing too earth-shattering. There's a lot of whining and whinging on the message boards, but take it with a grain of salt: the instructions to succeed in the programing assignments are complete and relatively simple, but you might have to dig around in lecture transcripts to put all the puzzle pieces together. The is GRADUATE LEVEL work, don't expect to be spoon-fed, and don't whine when you're not. I'd recommend the content to anyone. SO WHY ONLY 1 STAR? Because there is absolutely no support from TAs or Mentors anywhere. Nada. Zero. Zilch. They are asleep at the switch. If you expect any kind of interaction to expand your learning horizon then you will be sorely disappointed. I sure was. The lack of engagement from the TA/Mentor community takes what could have been a 5 star experience and drops it to zero. But I can't go that low, so 1 star it is.

This course is poorly structured, the material is poorly explained, the lecturer is going too fast and does not stress important concepts, video, and sound quality are below average. Do not recommend.

The structure of this course is an example of how not to teach mathematics. Examples before definitions and introduction of general concepts, lack of direction and "big picture" context, unexcusable things like "let's prove it by example"... It is very frustrating and almost impossible to follow.

The course contents and presentation is of very high quality. The assignments and quizzes are both challenging and very rewarding. The only minor qualm is that the programming assignment grader seems to have few issues. For one, MATLAB indexing is really hard to work with. Secondly, it doesn't test the answers fully in some cases. Like the case of OptimizeWithJointUtility, OptimizeLinearExpectations. My codes passed the grader but I was splitting to hair to figure out why my answers to quiz questions corresponding to programming assignment were wrong. Turned out that my code was incorrect for the two programming assignments and that was causing issues. Otherwise, really nice course. Thank you :).

A five stars course. Prof. Koller is an outstanding scientists in this field. The first part just introduce you two basic frames of graphical models. So go further into second part is necessary if you want to have a bigger picture. The whole course is an introduction to the book - Probabilistic Graphical Models of Prof. Koller, so buying her book is also highly recommended. This course is supposed to be hard, so you should expect a steep learning curve. But all the efforts you made are worthy. I suggest coursera will consider put more challenging exercises in order to extent the concentration. Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.

The course is very well organized and good leveled. The contents you get from the videos need to be completed/understood with the book. This makes this course a hard one, but very enjoyable. Having said that, I would suggest some improvements, if I am allowed. The first one is to update the course material to reflect the current scope of machine learning (e.g., Deep Learning). The second one is to include the option to code in Python. The last one is related to the final grade. I believe that giving the 24hs submission option is exaggerated. I really enjoyed the course and got a vision on PGM that will allow me to apply them in my work.

it's really hard course for me but after completing and see the certificate I feel so good about it. Yesterday someone asked a question regarding conditional independence. I remember before I took the course I've spent quite some time understanding it, just like him. But yesterday I didn't event think about it and gave him the right answer using "active trail" and "D-separation" concept. That's how powerful this course can be.

I didn't work on the honor track though because I'm currently short of time. But I think I will come back and taking the other 2 courses in this series.

Awesome intro to graphical models, and the exercises really emphasize understanding and proceed at what seems like the appropriate pace. Challenging for sure, you need to want to learn this stuff. Only downside is I'm not a fan of using octave/matlab--really wish this could be rebuilt using python for all the exercises. I've probably spent 60% of my time devoted to this course on getting that setup working and wrestling with telling the computer to do what I want in an unpopular language--at least, unpopular out in the world outside of academia.

This course is quite interesting not that easy. It helps me understand Markov network. The questions within the video are very helpful. It helps me check out some essential concepts and details. What's more, I'm fascinated by the teacher's voice and her teaching style, though detailed reading is required off class to gain comprehensive understanding. This is the first time I take online course in courser, and it's fun. I think I'll keep on learning the rest 2 courses of this series.

Excellent course for picking out just the critical portions of the Koller & Friedman book (which is over 1000 pages long, forget about reading it cover to cover for self study). Don't skip the programming assignments, they're very important for solidifying your understanding. You'll spend at least 75% of the time fussing over the somewhat arbitrary and baroque data structures used to represent factors and CPDs in this course, but at the end it's worth the frustration.

The course was very fruitful. It is was not that easy of course, I think it is one of the most difficult courses on Coursera but it deserves to try it once, twice and as many as you can until you understand the idea behind the course. The exams and the honor assignments were so tricky and not that easy to solve. If you don't have a probabilistic background, I think first better for you to take a course in data analysis and probability.

Awesome class, the content is not too easy as most online courses. Still the instructor states the concepts clearly and the assignments aligns very well with the content to help me deepen my understanding of the concepts. The assignments are meaningful and challenging, finishing them gave me a great sense of achievement!!

It would be better if the examples in the classes could incorporate some industry applications.

Perhaps the best introduction to AI/ML - especially for those who think "the future ain't what it used to be"; the mathematical techniques covered by the course form a toolkit which can be easily thought of as "core", i.e. a locus of strength which enables a wide universe of thinking about complex problems (many of which were correctly not thought to be tractable in practice until very recently!)...

So happy to complete this one. It was tough - especially the programming exercises and mainly due to high degree of vague-ness and un-expressiveness of matlab/octave in contrast to e.g. Python or Scala. samiam was unexpectedly handy and usable. Very nice and educational piece of software. Excellent course - it's incredible how many Machine Learning models are expressed under the umbrella of PGMs.

Excellent introduction which covers a wide range of PGM related topics. I really liked programming assignments. They are not too difficult but extremely instructive.

Word of advice: although programming assignments are not mandatory, dare not to skip them. You will be missing an excellent learning experience.

Another useful advice: lectures are self-contained but reading the book helps a lot.

I had actually read the David Barber book before I took this course. The course provides a deep insight to the PGMs which is necessary if one wants to utilize it in real applications or as in my case in research works. Moreover, the language of the instructor is comfortably plain, especially when it comes to explaining somewhat complicated concepts. In general, it is highly recommended.