Probabilistic Graphical Models 3: Learning

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 lectures and terrible assignments. Forum is not helpful at all. In fact, the forum is dead and tutors do not exist. Programming assignments have too many errors which are known within the forum for 4 years but no one is fixing these mistakes. All in all, the topic is highly interesting but the implementation is deficient

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.

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.

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

Great course, though with the progress of ML/DL, content seems a touch outdated.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

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

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.

Good course, but the material really needs a refresh!

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