Opiniones y comentarios de aprendices correspondientes a Probabilistic Graphical Models 2: Inference por parte de Universidad de Stanford

good

I believe the course is well dictated, well organized, and follows Koller's book and so it has to be read along with the lectures. Yet, I also think that improvements are a must. The following are points I would suggest taking into account: (1) it is very frustrating to have a final exam that can only be taken every 24 Hs. The course is indeed intense so having this limitation does not improve the level. (2) practical assignments are hard to understand, no feedback is given, and sometimes I have the feeling that the instructions were cut out from some other larger more understandable assignment, maybe at Standford. It is sometimes very frustrating to do them without a clear connection to lectures or a clear understanding of what is being asked to do. Overall, I strongly suggest doing the whole specialization with accompanying material, e.g., Koller's or Murphy's books. Alone the lectures are not enough and good that so it is.

The course content is great. The lecturer is great as she explains intuitively! Unfortunately, the programming assignments are horrible. Code is being provided without any mentioning in the PDF problem sheet. Moreover, most of the functions provided are not commented at all. Testing and debugging your method is made incredibly difficult because of the cryptic infrastructure of the test samples and too many typos in almost every problem sheet, which does not even get corrected even though many course takers pointed out these typos years ago. Finally, the forum for discussions is basically dead. If you do not get something there is no hope for you but to give up because mentors are not available in the forum. All in all, this class is really great but does not deliver enough content and information in order to be able to solve the programming assignment problems.

Unfortunately, in my opinion, this course is not as well structured as the first course (PGM1: structure). There are some bugs/issues with the PAs code that should have been fixed and the course material could focus a bit more on the case of continuous random variables (which are almost ignored throughout the course). It is still a great and totally worth it course, though. Highly recommended for machine learning post-graduate students.

The material is quite good and a good depth for a first pass. I would definitely have liked that there be some structure slides at the start of the lecture set. Saying -- this is what we will learn in week 1 week 2.. so on, so I know what I am getting into. The way it is designed now, I am swimming in the water so deep that I can barely see 1 week away.

Great Course, not five stars just because probabbly it was too much content for the period of time we had the Course. I've got no complaints about the amount of content, but some of concepts were missing and the Programming Assignments were not so well described, sometimes I couldn't understand what to do.

The course reminds me of my math lessons: lots of formulas and apparatus but little motivation (except in the optional videos). As in the first part of the specialization the advised book about PGM is highly recommended. To pass the final exam the book or at least some research papers are necessary (-1).

it is a great class. but the presentation of the materials could be better: maybe each unit should start with a review of the key concepts we learned before? maybe a slide on motivation of the work before we dive deep into the math? but again, this is a great class! recommended 100%

Unlike other Coursera courses, this specialization covers a lot of conepts accompanied with programming assignments. Since the programming assignments are pre-filled, its a bit tough to understand the style. It would be great if some form of explanation if offered.

A great course, and programing assignments add *a lot* of value to it. As with the other courses of this specialization, there is virtually no assistant support in discussion forums and very little discussion in general.

Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well

Great course. The assignments are old and are not worth doing it. But the content is good for those who are interested in Probabilistic Graphical Models basics.

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

Great introduction.

It would be great to have more examples included in the lectures and slides.

very good course

Thumbs up for the course content.

However, there are technical problems which no one is attending to. I could not submit my programming assignment, and after consulting every available resource, I was not dignified with an answer. It is a shame how such wonderful learning opportunity can become spoiled by some insignificant technical detail.

By my opinion, the course should not be divided into 3 courses. Many technicalities were done sloppy in the process.

I enjoyed learning about this exciting field. Though, the explanations need some more examples to generalize. Also, I found that there is a big gap between the videos and the programming assignments. Either the programming assignments get more theoretical explanations, maybe with some examples too, or the videos get more applied than they are now.

The last programming assignment is not very well designed. Also, I think that it would be better if more time was spent designing networks instead of learning the theory.

有幸能听到COＵＲＳＥＲＡ创始人的课，确实领略了一下大牛人的风采。但是从教课这个层面来看，　我相信有人能教得更好。　最可惜的是编程作业，我根本不能ｓｕｂｍｉｔ　。上课的内容和作业脱节很明显。　而且很多时候，　基本没有编程方面的支持（可以从论坛的人气就可以看出了），　学生几乎无从下手总的来说，此课过多的侧重于抽象层面的东西。

Content is good but honours assignments are unclear and no help from mentors in the discussion forums - more time-consuming than they should be

The Programming assignment must be updated and become relevant... They are way too hard and not friendly...

Great but it would be nice to have some introduction to approximate inference methods as well.

Can't get score for free!!!