I was very lost with the different depths of lectures and assignments in this part of the course. I felt that some places were super involved mathematically and was trying to understand its implication. In other places it felt like a lot of fluff. I would recommend this only if you have taken the other 2 parts. Also Prof. Koller's lectures are quite confounding and monotonous in these more than the other lectures.

A great course! Learned a lot. Especially the assignments are excellent! Thanks a lot.

Bad choice of content. Focus too much on the specific case of table CPDs, missing the big picture.

I love this course! It's very difficult but worthy. If you are looking for the state-of-the-art AI techniques, PGM doesn't seem to be your best choice. It's some kind of old fashion compared to DL. I learned a lot about the probability theory through all three courses, and I get better understanding with CRF and HMM. Seriously, it's not a course that will improve your skills or guarantee your successful immediately in ML fields, but a course that can shape your thoughts, help you think out of box. So if you don't like the black-box in DL, PGM will offer you another brand new perspective to understand this uncertain world.

The course is very involved but Daphne makes its palatable. The course open a new world of new possibilities where one can apply PGMs to get concrete understanding of relationships between events and phenomena in any discipline; from social sciences to natural sciences.

Great course, great assignments I indeed learn much from this course an the whole PGM ialization!

Excellent!

Very interesting course. Several methods and algorithms are well-explained.

Very challenging and fulfilling class!

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.

Tougher course than the 2 preceding ones, but definitely worthwhile.

Yeah! I managed to finish PGM. I feel ready to explore further. PGM 3 is really helpful. Although many details are not fully discussed, some important intuitions are well illustrated, like EM algorithm and its modification in case of incomplete data. Also, the way the teacher teach set an good example for me to learn to demonstrate complicated things in an easy and vivid way. Thank you so much!

Thank You for all.

Awesome course... builds intuitive thinking for developing intelligent algorithms...

Excellent course! Everyone interested in PGM should consider!

The course facilitates learning - and reinforces acquired knowledge through the simple principle of honest effort: students are not given all the answers... but they are 'nudged' in the right direction & guided towards fruitful questions; in a way, it's the perfect course!

Very useful course.

Had a wonderful Experience, Thank you Daphne Ma'am

Excellent course. Programming assignments are excellent and extremely instructive.

Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

The course really helps me understand a lot of things about learning graphical model, from estimating parameters for Bayesian Network, Markov Random Field, CRF, to learning graph structure from data and using EM algorithms to learn when there is missing data. It also gives many guidelines about the process of machine learning in general. I found the programming assignment more challenging than the first 2 parts but at the same time they are very enlightening when all the pieces beautifully fit together. In general, it was a fun, challenging and enlightening learning experience. I want to thank the course instructor and staffs who made this great course possible.

Great course, very helpful.

Great course, though with the progress of ML/DL, content seems a touch outdated. Would

Great content. Explores the machine learning techniques with the tightest coupling of statistics with computer science. The Probabilistic Graphical Models series is one of the harder MOOCs to pass. Learners are advised to buy the book and actually read it carefully, preferably in advance of listening to the lectures. The quality of the course is generally high. The discussion is a little muddled at the very end when practical aspects of applying the EM algorithm (for learning when there is missing data) is discussed.