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Вернуться к Probabilistic Graphical Models 2: Inference

Отзывы учащихся о курсе Probabilistic Graphical Models 2: Inference от партнера Стэнфордский университет

Оценки: 341
54 рецензий

О курсе

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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....

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Mar 12, 2017

Thanks a lot for professor D.K.'s great course for PGM inference part. Really a very good starting point for PGM model and preparation for learning part.


May 29, 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

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1–25 из 54 отзывов о курсе Probabilistic Graphical Models 2: Inference

автор: Shi Y

Dec 16, 2018

It's absolutely very very hard but extremely interesting course! Although code assignments always have a lot of small bugs, and it cost me lots of time to find out, but, hey! Everything is the same in school(offline), nothing gonna be perfect. The sampling part is the most difficult stuff to learn so far, and after I tried to review it again and again, combined with other online material, I got those shit done! The only drawback of this course is that not many people active in the forum(Including those TA), maybe that just because only a small number of people enrolled in this course. In short, worth learning!

автор: Kaixuan Z

Dec 05, 2018

hope to get some feedbacks about hw or exam

автор: Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

автор: Tianyi X

Feb 23, 2018

not very clear from the top-down level.

автор: george v

Nov 28, 2017

great course, though really advanced. would like a bit more examples especially regarding the coding. worth it overally

автор: Anurag S

Nov 08, 2017

Great introduction to inference. Requires some extra reading from the textbook.

автор: Jonathan H

Aug 04, 2017

Pretty good course, albeit very dense compared to the first one (which was certainly not trivial). I would give it 5 stars just based on the content, but the programming assignments don't work without significant extra effort. I completed the honors track for the first course, but gave up after spending 4 hours trying to fix HW bugs that were reported 8 months ago.

Would have also been nice to have more practical examples to work on. Some of the material is very theoretical, and I find it hard to build intuitions without applying the algorithms in practice.

автор: Amine M

May 14, 2019

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.

автор: Phillip W

May 01, 2019

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.

автор: Akshaya T

Mar 14, 2019

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.

автор: Mahmoud S

Feb 22, 2019

The honorary assignments contain code mistakes, and difficult to do! You are sifting through mistakes in the instructions along with the supplemented code!

автор: Lik M C

Feb 03, 2019

Very great course! A lot of things have been learnt. The lectures, quiz and assignments clear up all key concepts. Especially, assignments are wonderful!

автор: 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.

автор: Kalyan D

Nov 05, 2018

Great introduction.

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

автор: Musalula S

Aug 02, 2018

This is a great course

автор: Luis

Aug 01, 2018

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

автор: Gorazd H R

Jul 07, 2018

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

автор: Tomer N

Jun 20, 2018

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

автор: hanbt

Jun 08, 2018

Very good

автор: Julio C A D L

Apr 09, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

автор: Liu Y

Mar 18, 2018

Really a interesting, challenging and great course!

автор: Evgeniy Z

Mar 10, 2018

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.

автор: Wei C

Mar 06, 2018

good way to learn PGM,

автор: chen h

Feb 06, 2018

Interest but difficult.

автор: Chan-Se-Yeun

Jan 31, 2018

I kind of like the teacher. She can always explain complicated things in a simple way, though the notes she writes in the slides are all in free style. Loopy belief propagation and dual decomposition are the best things I've learnt in this course. I've met them before in some papers, but I found it extremely hard to understand then. Now I gain some significant intuition of them and I'm ready to do further exploration. Anyway, I'll keep on learning course 3 to achieve my first little goal in courser.