Jul 13, 2017
Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!
Oct 23, 2017
The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).
교육 기관: Péter D•
Oct 29, 2017
great job, although the last PA is a huge pain / difficulty spike - more hints would be nice
교육 기관: george v•
Jul 07, 2017
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!
교육 기관: Shawn C•
Nov 05, 2016
The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.
교육 기관: Antônio H R•
Jun 20, 2018
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).
교육 기관: Jonathan H•
Jun 25, 2017
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.
교육 기관: Roland R•
Dec 20, 2017
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.
교육 기관: Vincent L•
Mar 21, 2018
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.
교육 기관: Arthur B•
Jan 08, 2017
More feedback from TA would be appreciated
교육 기관: Roman S•
Mar 20, 2018
A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.
교육 기관: Werner N•
Dec 28, 2016
Very good course. It should contain more practical examples to make the material better to understand.
교육 기관: Kevin W•
Jan 17, 2017
The course is pretty good. I love the way that the professor led us into the graphical models.
교육 기관: Haitham S•
Nov 24, 2016
Great course, however, the honors track assignments are a bit too tedious and take lots of time.
교육 기관: 邓成标•
Nov 30, 2017
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.
교육 기관: Tianyi X•
Feb 20, 2018
Lack of top-down review of the PGM.
교육 기관: Hanbo L•
Apr 30, 2017
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.
교육 기관: Hunter J•
Jan 12, 2017
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.
교육 기관: Luiz C•
Jun 26, 2018
Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory
교육 기관: Michael K•
Nov 14, 2016
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.
교육 기관: Rajeev B•
Dec 23, 2017
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.
교육 기관: Ludovic P•
Oct 29, 2017
I wish I could give 4 and a half star to this course.
On the positive side : there is a lot of value in this course. Professor Koller succeeds in introducing us to PGM representations in a few weeks. IMHO, one should really do all the exercices "for a mention". Without them, this course lacks "hands on" sessions, and is much less interesting. Most programming exercises are great, and the companion quiz are really a plus.
When I followed Professor Ng programming exercises, I was both delighted and frustrated. Delighted because I learned a lot of things. Frustrated because it was sometimes really too easy.
This is not the case for most exercices there. I find them so well prepared, so crafted that I often learned a lot of my first wrong submissions of quiz of programming exercices.
On the negative side : the quality of the sound recordings is sometimes not really good. That is especially true in the first videos. That should not stop you from following this great course ! Some programming exercices were a bit frustrating because their difficulty is more in knowing octave tips and tricks than in PGM. In addition, and this is more embarassing, some exercices do not work, like in Markov Network for OCR https://www.coursera.org/learn/probabilistic-graphical-models/programming/dZmtj/markov-networks-for-ocr I had, as other students, to disable some features and to blindly submit my ansmwers.
Also, some exercises were difficult for me because of very precise English. I guess it might be difficult for native speakers to handle that, but as this course seems to have an international audience, it would be great.
I feel that raising this great course from 4 stars to 5 stars would not require much efforts. Prepare better recordings of the few videos that have really bad sound. Correct those small bugs in exercises. Simplify some English wordings.
I, however, advise this course to all persons interested in this field. And I intend to follow the next course, on inference.
교육 기관: Mehmet U•
Jul 02, 2017
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.
교육 기관: Rick d W•
Apr 20, 2017
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.
교육 기관: Gorazd H R•
Jul 07, 2018
A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.
교육 기관: firstname.lastname@example.org•
Oct 04, 2016
Great and well paced content.
Quizzes really helps nailing the tricky points.
교육 기관: Caio A M M•
Dec 03, 2016
Instructor is engaging in her delivery. Topic is interesting but difficult.