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Comentários e feedback de alunos de Probabilistic Graphical Models 1: Representation da instituição Universidade de Stanford

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1,364 classificações
304 avaliações

Sobre o 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 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....

Melhores avaliações

ST
12 de Jul de 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!!

CM
22 de Out de 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).

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176 — 200 de 297 Avaliações para o Probabilistic Graphical Models 1: Representation

por Yang P

26 de Abr de 2017

Great course.

por Nairouz M

13 de Fev de 2017

Very helpful.

por brotherzhao

15 de Fev de 2020

nice course!

por Utkarsh A

30 de Dez de 2018

maza aa gaya

por Musalula S

2 de Ago de 2018

Great course

por Yuri F

15 de Mai de 2017

great course

por 赵紫川

27 de Nov de 2016

Nice course.

por Pedro R

9 de Nov de 2016

great course

por Frank

14 de Dez de 2017

老师太天马行空了。。。

por HOLLY W

24 de Mai de 2019

课程特别好,资料丰富

por Siyeong L

21 de Jan de 2017

Awesome!!!

por Alireza N

12 de Jan de 2017

Excellent!

por dingjingtao

7 de Jan de 2017

excellent!

por Phan T B

2 de Dez de 2016

very good!

por Jax

8 de Jan de 2017

very nice

por Jose A A S

25 de Nov de 2016

Wonderful

por mohammed o

18 de Out de 2016

Fantastic

por zhou

13 de Out de 2016

very good

por 张浩悦

22 de Nov de 2018

funny!!

por Alexander A S G

9 de Fev de 2017

Thanks

por oilover

2 de Dez de 2016

老师很棒!!

por 刘仕琪

31 de Out de 2016

不错的一门课

por Accenture X

12 de Out de 2016

Great

por Ludovic P

29 de Out de 2017

I wish I could give 4 and a half star to this course.

On the positive side : there is a lot of value in this course. Professor Koller succeeds in introducing us to PGM representations in a few weeks. IMHO, one should really do all the exercices "for a mention". Without them, this course lacks "hands on" sessions, and is much less interesting. Most programming exercises are great, and the companion quiz are really a plus.

When I followed Professor Ng programming exercises, I was both delighted and frustrated. Delighted because I learned a lot of things. Frustrated because it was sometimes really too easy.

This is not the case for most exercices there. I find them so well prepared, so crafted that I often learned a lot of my first wrong submissions of quiz of programming exercices.

On the negative side : the quality of the sound recordings is sometimes not really good. That is especially true in the first videos. That should not stop you from following this great course ! Some programming exercices were a bit frustrating because their difficulty is more in knowing octave tips and tricks than in PGM. In addition, and this is more embarassing, some exercices do not work, like in Markov Network for OCR https://www.coursera.org/learn/probabilistic-graphical-models/programming/dZmtj/markov-networks-for-ocr I had, as other students, to disable some features and to blindly submit my ansmwers.

Also, some exercises were difficult for me because of very precise English. I guess it might be difficult for native speakers to handle that, but as this course seems to have an international audience, it would be great.

I feel that raising this great course from 4 stars to 5 stars would not require much efforts. Prepare better recordings of the few videos that have really bad sound. Correct those small bugs in exercises. Simplify some English wordings.

I, however, advise this course to all persons interested in this field. And I intend to follow the next course, on inference.

por Jonathan H

25 de Jun de 2017

Excellent course. The video lectures are challenging (had to keep my finger on the pause key) even if you're familiar with the math, since the instructor encapsulates concepts in an amazingly concise manner. This pays off with a lot of "Aha!" moments as strong concepts are combined to create insights, especially starting around week 3. I'm already in love with this subject after 1 part

It would have been nice to have more worked homework problems, since this is a math course. But, this is not necessary to pass the class or understand the concepts. I've purchased Prof Koller's text on PGM and hope to solidify some of the intuitions I'm missing shortly.

Taking off a star because the test cases and grading software for the honors homework assessments were clearly low effort and sometimes incorrect. There were a lot of cases where functions passed all the provided and automatic test cases despite major flaws (e.g. not working for any cases besides n=1), which made it difficult to tell if things worked since the programming style is unique. The homework itself was super interesting and valuable, but I probably spent over 50% of the time fighting the grader instead of learning stuff. Given that I'm a professional programmer and completed most of the homework in 25-50% of the estimated time, I'm guessing that the average student wasted even more time with issues that are ultimately unrelated to our understanding of PGM.