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....

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.

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.

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.

Top notch course! I only wish the explanations for answer choices in the quizzes/exams were more elaborate, as some of them are single sentences that don't really provide justification.

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

concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos

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

Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines

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

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

The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

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

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

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.

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.