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學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋

1,295 個評分
287 條評論


Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems. This course is the first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....



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).


251 - Probabilistic Graphical Models 1: Representation 的 275 個評論(共 280 個)

創建者 Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

創建者 Sunil

Sep 12, 2017

Great intro to probabilistic models

創建者 Nikesh B

Nov 06, 2016


創建者 Tianqi Y

Jun 20, 2019

too hard

創建者 Yashwanth M

Jan 05, 2020


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 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

創建者 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.

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 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 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!

創建者 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.

創建者 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.

創建者 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.

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 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.

創建者 roma 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

創建者 Xingjian Z

Nov 02, 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

創建者 Ujjval P

Dec 13, 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

創建者 Jonathan K

Jan 26, 2018

Interesting and useful material, but I found the lecturer unengaging.

創建者 Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

創建者 Robert M

Feb 06, 2018

Started off well. Finished poorly

創建者 Peter

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 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.

創建者 Andrew M

Aug 24, 2020

The course content is solid. The honours content is challenging and interesting. There's a couple of minor glitches that cause frustration in the PA's but nothing too earth-shattering. There's a lot of whining and whinging on the message boards, but take it with a grain of salt: the instructions to succeed in the programing assignments are complete and relatively simple, but you might have to dig around in lecture transcripts to put all the puzzle pieces together. The is GRADUATE LEVEL work, don't expect to be spoon-fed, and don't whine when you're not. I'd recommend the content to anyone. SO WHY ONLY 1 STAR? Because there is absolutely no support from TAs or Mentors anywhere. Nada. Zero. Zilch. They are asleep at the switch. If you expect any kind of interaction to expand your learning horizon then you will be sorely disappointed. I sure was. The lack of engagement from the TA/Mentor community takes what could have been a 5 star experience and drops it to zero. But I can't go that low, so 1 star it is.