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Volver a Probabilistic Graphical Models 2: Inference

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

455 calificaciones
69 reseña

Acerca del Curso

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....

Principales reseñas

22 de ago. de 2019

Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

19 de ago. de 2019

I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.

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51 - 71 de 71 revisiones para Probabilistic Graphical Models 2: Inference

por Amine M

14 de may. de 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.

por Diogo P

24 de oct. de 2017

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.

por Akshaya T

14 de mar. de 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.

por Diego T

9 de jun. de 2017

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.

por Michael G

14 de dic. de 2016

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).

por Siwei G

15 de jun. de 2017

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%


23 de dic. de 2017

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.

por Maxim V

5 de may. de 2020

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.

por Luiz C

31 de jul. de 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

por Rishabh G

16 de may. de 2020

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.

por Gorazd H R

7 de jul. de 2018

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

por Kalyan D

5 de nov. de 2018

Great introduction.

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

por G.K.Vikram

24 de jul. de 2017

very good course

por ivan v

31 de jul. de 2017

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.

por Phillip W

1 de may. de 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.

por Jesus I G R

15 de oct. de 2019

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.

por Siwei Y

17 de ene. de 2017

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。

por Chris V

13 de dic. de 2016

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

por Tomer N

20 de jun. de 2018

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

por Thomas W

5 de may. de 2017

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

por fan

19 de nov. de 2016

Can't get score for free!!!