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 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.
創建者 Robert M•
Feb 06, 2018
Started off well. Finished poorly
創建者 Jonathan K•
Jan 26, 2018
Interesting and useful material, but I found the lecturer unengaging.
創建者 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.
創建者 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.
創建者 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).
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)
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.
創建者 Benjamin B•
Apr 12, 2018
Did not like how the concepts were introduced, it felt like learning theory for the sake of theory.
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.
Jun 24, 2017
Lectures are awful.
創建者 Belal M•
Sep 08, 2017
A very dry course.