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).
par Yashwanth M•
Jan 05, 2020
par Paul C•
Oct 31, 2016
I found plenty of useful information in this course overall but lectures often spent too much time dwelling on the detail of simpler concepts while more complex areas, and sometimes critical information that was later built upon, were only touched briefly or sometimes skipped entirely. I missed a sense of continuity as we skipped from model to model with a minimum of time spent on how the models complement each other and their relative strengths and weaknesses in application.
The way data structures were defined in the code was particularly difficult to deal with. The coding exercises all suffered as a result. It ended up taking way too much time to figure how to decode the data and trace logic around it. This meant that grasping concepts and learning from the questions came in a distant second priority to debugging.
Dr Koller mentioned that the material is aimed at postgraduates. I felt that the level of content covered here would just as easily be grasped by most undergraduates in technical disciplines if it had been delivered in a more structured manner with clearer progression across models (conceptually and mathematically) and better code examples. When delivering in this format, allowances need to be made for the facts that tutorial sessions do not exist and the possibilities for informal Q&A are limited so any gaps become very difficult for students to fill in themselves.
Despite the above shortcomings I'm glad I did the course and I would still recommend it to someone interested in graphical models as it does cover the basics well enough to make a decent start. I'm not sure whether or not I'd recommend the programming exercises as they are a significant time sink but at the same time, without spending time attacking the programming problems the concepts are not likely to gel based on the video and quizzes alone.
par Nicholas E•
Oct 29, 2016
The course was very interesting and thought-provoking. I found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism. The discussions highlighted exciting aspects of their power in simplifying complex problems involving uncertainty. However, I still do not feel I could propose convincing PGMs for real-world problems. There are examples in the course, but they are far removed from being concrete applications. I would have preferred there be an in depth analysis of an application of PGMs in the literature over the lengthy programming assignments. I am an experienced programmer with over 5 years of experience in many languages including MATLAB/Octave and I sometimes found it uninspiring to solve toy problems, not due to the difficulty in using the programming language, but rather because after the assignment had been completed I felt I had not really learnt much more than I would have from just watching the lectures, although, if you are interested in getting experience with MATLAB/Octave, the programming assignments are good practice. I qualify this in stating that I have not yet completed the next two courses on PGMs; this course may present an essential foundation that is necessary for the upcoming courses, and in any case provoked my interest in learning more about them
par Mahendra K•
Oct 04, 2017
The course is highly theoretical. Would have been great if it was paced well and driven from real world examples. I am not saying that there are no examples. But it'd have been better if the concepts were driven via some real world examples instead of first talking about the concept and then its applications.
What would have been even better if Python was an option for PAs. Octave can't be used in industry setting where the amount of data is really large. Both Python and Octave should have been an option so that the student can decide for themself.
par John E M•
Apr 01, 2018
Lectures were OK and quizzes and exams appropriately difficult. But Labs were pretty difficult especially lab 4 which I ended up surrendering on. This means I didn't do the accompanying quiz and gave up on the possibility of honors recognition as well.
While labs don't have to be as hand-holding as the DeepLearning class by Coursera, it would be nice to get more help and maybe not submit errors for the parts I haven't tackled yet when submitting (as DeepLearning and MachineLearning courses figured out how to do).
par Kervin P•
Jan 05, 2017
This is an amazing course, and taught by an extremely talented and accomplished professor. I believe it's a must for anyone in AI/ML or Statistical Inference. The problem is that you're essentially on your own the entire course. There isn't any community or TA help to speak off. And the project is done in Matlab, so you end up wrestling with Matlab or Octave instead of actually doing and learning. I still recommend the course, but that's only because the material is so extremely important.
par Daniel S•
Dec 11, 2019
Prof. Koller is exceptional. However, the focus of the course is toward the "theory" and less towards applications, unless one chooses to complete the Honors section of the course. I personally did not have the time to learn a new language syntax to attempt the Honors section...which is a shame. I do hope that this course is updated where R/Python replaces Octave/MatLab, because it would allow professional analysts more opportunity to explore the Honors content. Thanks!
par Volodymyr D•
Apr 11, 2020
Useful course on great subject, but poorly explained and supported. It was quite hard for me to get implicit ideas and Honors assignments. I ended up skipping Honors assignments since they're explained really really poorly and most of the time I spent trying to figure out what I'm required to do. Forums are inactive and no mentors reply to the posts. I don't recommend taking this course if you don't have someone to guide and help you.
par Sami J•
Apr 22, 2020
Material is interesting but needs updating. Programming assignments have been marked as "Honors Assignments", which is a thinly veiled attempt to shirk responsibility for fixing bugs and providing student support. Quiz questions are vaguely worded. Overall the course is challenging, but only sometimes for the right reasons.
par Shen C•
Jul 14, 2020
this course is a very difficult one. takes a lot of time and effort. forum is really useful (i wouldn't have passed without it). that said, it is also because there is little help from the lecturer and instructors. would appreciate more help.
par Christos G•
Mar 09, 2018
Quite difficult, not much help in discussion forums, some assignmnents had insufficient supporting material and explanations, challenging overall, I thought at least 3-4 times to abandon it.
par Siavash R•
Aug 11, 2017
For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.
par Roman G•
Nov 04, 2016
The audio is VERY VERY poor.
That makes it very hard to understand what Prof Kohler is trying to impart on us..
I often lost track
par Xingjian Z•
Nov 02, 2017
Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.
par Ujjval P•
Dec 13, 2016
Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.
par Jonathan K•
Jan 26, 2018
Interesting and useful material, but I found the lecturer unengaging.
par Michel S•
Jul 14, 2018
Good course, but the material really needs a refresh!
par Robert M•
Feb 06, 2018
Started off well. Finished poorly
Sep 29, 2016
The content seems to be excellent regarding "what" is presented. But sadly the sound quality is rather bad: Sounds like an age-old valve radio with A LOT of dropouts. And Professor Daphne is an agile and therefore less disciplined speaker which lessens the understandability of her speech in conjunction with the poor sound quality furthermore. Especially for me as a non-native foreign english speaker it is very hard to follow. And now I am at one point in the course, that is "Flow of Probalistic Influence", where she explains a concept without explaining what is meant with the used underlying notions "flow" and "influence" which makes me difficult to understand what is going on. That means in my point of view that the slides are not sufficiently prepared. Although I'm very interested in the topic I am asking myself after the first view videos if I should continue or drop because my cognitive capacitity is for me to worthful to use it for the decoding of badly prepared and presented material. Ok, my decision heuristic in such cases is "Use the hammer not the tweezers!". Therefore I have dropped. Please improve the state of this class from beta to release. Then I will come back.
par Jennifer H•
Dec 16, 2019
Quite abstract. A solid mathematical grounding, but largely devoid of practicalities. Optional exercises are quite basic, and don't get to the heart of the matter. Lectures are confusing, as undefined terminology come up out of the blue, and key concepts aren't clearly explained.
par Benjamin B•
Apr 12, 2018
Did not like how the concepts were introduced, it felt like learning theory for the sake of theory.
par Ahmad C•
Jun 11, 2020
very shallow explanation of important concepts
Jun 24, 2017
Lectures are awful.
par Belal M•
Sep 08, 2017
A very dry course.
par Francisco J G•
Aug 04, 2020