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).
創建者 Alberto C•
Dec 01, 2017
Theory: Very interesting. Assignments: not so useful.
創建者 Caio A M M•
Dec 03, 2016
Instructor is engaging in her delivery. Topic is interesting but difficult.
Nov 09, 2017
This is a tough course so it was split into 3 parts. I've learned some ideas about bayesian network and markov model. The major problem about this course is the programming assignment, which is poorly maintained. Daphne Koller is very brilliant but this makes it hard for people to catch up with her, especially for people whose mother language is not English. After all, this is an interesting course!
創建者 Andres P N•
Jun 27, 2018
There are many error in the implementations for octave. Aside from that, the course is fine
創建者 Soteris S•
Nov 27, 2017
A bit more challenging than I thought but very useful, and very well structured
創建者 Forest R•
Feb 20, 2018
Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.
創建者 Jack A•
Nov 05, 2017
The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.
創建者 Boxiao M•
Jun 28, 2017
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.
創建者 Ahmad E•
Aug 20, 2017
Covers some material a little too quickly, but overall a good and entertaining course.
創建者 Zhen L•
Nov 16, 2016
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....
創建者 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.
創建者 Arthur B•
Jan 08, 2017
More feedback from TA would be appreciated
創建者 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.
創建者 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.
創建者 Kevin W•
Jan 17, 2017
The course is pretty good. I love the way that the professor led us into the graphical models.
創建者 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).
創建者 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.
創建者 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.
創建者 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.
創建者 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.
創建者 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.
創建者 Haitham S•
Nov 24, 2016
Great course, however, the honors track assignments are a bit too tedious and take lots of time.
創建者 Werner N•
Dec 28, 2016
Very good course. It should contain more practical examples to make the material better to understand.
創建者 Akshaya T•
Jan 16, 2018
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.
創建者 Jhonatan d S O•
May 25, 2017
Rich content and useful tools for applying in real problems