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 Sandeep M•
Sep 23, 2018
The content of the course is good but the assignments are in matlab which isn't as widely used as python and has the additional headache of licensing. it is the assignments where you really learn things so this is a serious negative point.
創建者 Ben L•
Jan 13, 2019
Would be better if there are people monitoring the discussion board and actually answer student's questions.
創建者 Amine M•
Apr 30, 2019
The material is really important and helpful for many concepts of Machine Learning. Daphne Koller is very good at explaining complicated ideas in an intuitive way. The programming assignments are very relevant and cover many real-world application scenarios in medical diagnosis and testing. Unfortunately, programming assignments have many flaws. First, some scripts do not work and therefore it is necessary to manually adjust these in order to submit your assignment part by part. Second, the forum is almost dead, which means that is is difficult to get help once you are stuck at a problem. Most of the helpful posts are almost two years old. Third, often times questions in the quiz are very vague and not clearly formed which makes it difficult to answer the instructor's question. All in all, I think, that the course is worthwhile but nonetheless the course definitely needs some refurbishing and bugs in scripts need to be fixed.
Jan 06, 2018
Good course, with actual university level content and depth (albeit in a multiple choice format). The explanations of the material were clear, however if you don't have at least a surface level familiarity with Bayesian probability and first year university level math, you'll find yourself spending a lot of time looking up random jargon on Wikipedia.
If you lack the necessary background, I suggest reviewing the content of Stanford CS109 (the content is publically available).
The assignments were a bit opaque / wordy; instead if an essay, provide clear bullet point tasks with a detailed appendix for clarity. Also, please use Python instead of Matlab. It's free, there's a more support available for it, it has much clearner syntax, much more comprehensive libraries and it's at least tollerably performant (in comparison to Matlab / Octave).
創建者 Michael S E•
Feb 14, 2017
This course was solid overall but not excellent. I learned the basics of different classes of probabilistic models including Bayesian networks and Markov networks and how to represent them. Prof. Koller is knowledgeable and presented the materially logically. With that said, this course could have been a lot better than it was.
The honors programming assignments could have been excellent The material was interesting and dovetailed well with the course content. But the assessment process was very frustrating and led to a lot of wasted time debugging that was geared more to quirks of the grader than to course concepts. Both test cases and feedback on failed submissions were woefully inadequate. Some of the quizzes were also frustrating, featuring what I consider to be "gotcha" questions geared more to creating a grading curve than to measuring understanding of the material.
Advice to course staff: (1) Please provide more test cases on coding assignments (2) Please provide better feedback in submission reports (3) Please monitor the discussion boards more actively for unanswered questions (4) If you want to provide an externally linked executable you intend students to run from Matlab, it's not reasonable to give a 32 bit file in 2017 and send us down a rabbit hole where you suggest we build the executable from source, which in turn requires us to build the boost library from source.
創建者 Alex L•
Apr 09, 2018
This is not an easy course, so beware. The instruction is solid but you still need to reason through a lot on your own, and especially if you choose to complete the Honors programming section (which I highly recommend to prove to yourself that you really understand what you have learned and can apply it), you really need to plan on allocating sufficient number of hours to work through the programming assignments. You'll likely need to re-watch several of the video segments several times for it to really sink in, as well as referencing the Discussion Forum when you are stuck and need inspiration. Once you do complete this course (after many hours of work and thought) you will enjoy a deep sense of accomplishment, will look and think about decision-making in a fresh new way, and have learned many very useful skills.
創建者 Alexander P•
Apr 02, 2019
I really enjoyed the content of this course. Having been inspired by reading The Book of Why, I was looking for some formal language around Bayesian Networks and this course really fit the bill. My biggest piece of feedback is on the programming assignments. These really should be in Python. Octave is an okay choice, and I suspect might have to do with Andrew Ng original choice to use it for his own machine learning course. However, the data science community writ large uses Python and R, which is why Andrew switched to Python for his deep learning courses. I would recommend the programming assignment be updated so that they are more accessible to the data science community.
創建者 Deleted A•
Nov 18, 2018
This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.
創建者 YUXUN L•
Dec 07, 2016
This course is really amazing. The lecture is well-organised and lecture material is good. This course covers basic knowledge about representation in Probabilistic Graphical Model. It includes Markov Network, Bayesian Network, Template Model and some other knowledge. The assignments, oh, I have to say, although some quiz in it seems like having bug, are still impressive. I strongly recommend finishing all the programming assignments of this course. Some trick parts of the knowledge taught in the course are covered by the assignments (like template model part, trust me you have to think about the template model part really, really carefully to figure out what it exactly means). Anyway, it worth my payment :-).
If you wanna take this course, buying a textbook is a good choice because there are some extra knowledge which is not covered by this course in the textbook. However, without a textbook you can still continue. I really appreciate Professor Koller for offering such a great, amazing course!
創建者 sergei s•
Jan 23, 2020
Wow! It was an amazing journey. Daphne Koller is an outstanding lecturer and I was very impressed with the quality of provided material. This course is the MUST TO HAVE if you study modern communication theory, where the probability-based approaches are widely used (receivers, estimation, TurboCodes, LDPC).The assignments are tough due to many unclear moments, that appear quite often. You need to analyse them regularly and I watched some lectures again few times. Since you need to extend a provided Matlab code, it is often required to debug and check how it works in details. And it forces you to learn implementation details and suplied libraries. Personally, I discovered libDAI, which is definitely an amazing tool.
Mar 13, 2018
In the video, a lot of knowledge point do not explain very clearly, we do not konw how to resolve the quizzes. Moreover, if buy the textbook, may acquire more detail about PGM, but the textbook do not explain very clear neither. Textbook is hard to read. Even so, this course is worthwile to learn. Because PGM is one of the basic theory of machine learning and widespread use. In the end, thank Koller and coursera! Thank you very much!
創建者 Santosh K S•
Jul 28, 2018
Dear Madam thanks a lot for the course.
This course - in addition to Machine Learning, by Andrew Ng Sir, are perhaps most comprehensive courses.
This course covers a lot over a period of 5 weeks. It demands higher level of focus. So, the learning still continues..
Santosh Kumar Singh
創建者 Dhruv P•
Jun 18, 2017
I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.
Thank You Daphne Mam. God Bless Everybody Associated with it.
創建者 Shi Y•
Nov 13, 2018
創建者 Tomasz L•
May 12, 2019
Great course! Lectures are clear and comprehensive. Quizzes really check knowledge and are challenging. In the programming assignments the main focus is put on implementation of PGM algorithms and not on technical aspects of Octave/Matlab. Some changes could be made in Programing Assignment 4 to make description and provided code easier to understand.
創建者 Phillip W•
Apr 08, 2019
Sometimes the questions weren't clear. But in general, I really like the course and the things I've learnt I am sure they are useful.
創建者 Lorenzo B•
Jan 19, 2019
The course contents are presented very clearly. Difficult ideas are conveyed in a precise and convincing way. Despite this, the global structure is not presented very clearly, and the quality of some course material is not excellent. In particular, I didn't find the optional programming assignments particularly interesting, and the code/questions contained more than one bug. Also, the quality of video/sound is quite poor, and varies a lot from course to course.
創建者 Michael G•
Feb 05, 2017
The support by the mentors could be much better. Because of the missing support I was not able to solve the assignments under Windows with Octave. I had to buy Matlab. (-2)
It seems to me that the course is very difficult to complete without additional sources. (-1)
創建者 Ashok S•
Mar 30, 2018
It is hard to follow the course without a book, and the book is expensive.
創建者 Alexey G•
Nov 06, 2016
It is impossible to submit quizes and programming assignments without purchasing the course. In my view this defies the goal of Coursera to provide accessible education anywhere in the world!
創建者 Casey C•
Nov 01, 2016
Superficial coverage of quiz and final exam material in the video lectures. Without getting the textbook and reading it in depth, it is difficult to do well in this class.
創建者 Sergey V•
Oct 28, 2016
Done! The #PGM class is probably one of the most challenging ones in Coursera both in terms of workload and theoretical depth. I used to spend 10+ hours per week and I doubt anyone could complete it successfully without Matlab knowledge and strong background in #probability #machinelearning and #programming. Comprehensive programming assignment with honour content and quizzes help to make yourself very familar with the topics: #bayesiannetwork #gibbssampling #intercasualreasoning #markovproccess #markovchain #OCR Daphne Koller @DaphneKoller , as Coursera co-funder, made her best to show the capabilities of the platform. To sum up, prospective students should take into account that the course is quite advanced and several background in probability, statistics, machine learning and algorithms required if you going to sign up for the PGM class =) Lectures and videos available for free but graded assignments and verified certifcate is paid option. Cheers, @RiddleRus #stanford #math #probability #probabilisticmodels P.S. I had spent at least five attempts before I passed a final assignment!
創建者 Sumod K M•
May 06, 2019
The course contents and presentation is of very high quality. The assignments and quizzes are both challenging and very rewarding. The only minor qualm is that the programming assignment grader seems to have few issues. For one, MATLAB indexing is really hard to work with. Secondly, it doesn't test the answers fully in some cases. Like the case of OptimizeWithJointUtility, OptimizeLinearExpectations. My codes passed the grader but I was splitting to hair to figure out why my answers to quiz questions corresponding to programming assignment were wrong. Turned out that my code was incorrect for the two programming assignments and that was causing issues. Otherwise, really nice course. Thank you :).
創建者 Ka L K•
Mar 27, 2017
A five stars course. Prof. Koller is an outstanding scientists in this field. The first part just introduce you two basic frames of graphical models. So go further into second part is necessary if you want to have a bigger picture. The whole course is an introduction to the book - Probabilistic Graphical Models of Prof. Koller, so buying her book is also highly recommended. This course is supposed to be hard, so you should expect a steep learning curve. But all the efforts you made are worthy. I suggest coursera will consider put more challenging exercises in order to extent the concentration. Finally, a highly respect to Prof. Koller who provide the course in such a theoretical depth.
創建者 Sha L•
Apr 20, 2017
it's really hard course for me but after completing and see the certificate I feel so good about it. Yesterday someone asked a question regarding conditional independence. I remember before I took the course I've spent quite some time understanding it, just like him. But yesterday I didn't event think about it and gave him the right answer using "active trail" and "D-separation" concept. That's how powerful this course can be.
I didn't work on the honor track though because I'm currently short of time. But I think I will come back and taking the other 2 courses in this series.