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Вернуться к Probabilistic Graphical Models 1: Representation

Отзывы учащихся о курсе Probabilistic Graphical Models 1: Representation от партнера Université de Stanford

4.7
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Оценки: 1,190
Рецензии: 256

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 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....

Лучшие рецензии

ST

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!!

CM

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).

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151–175 из 252 отзывов о курсе Probabilistic Graphical Models 1: Representation

автор: David D

May 30, 2017

Mind blowing!

автор: Yang P

Apr 26, 2017

Great course.

автор: Nairouz M

Feb 14, 2017

Very helpful.

автор: brotherzhao

Feb 15, 2020

nice course!

автор: Utkarsh A

Dec 30, 2018

maza aa gaya

автор: Musalula S

Aug 02, 2018

Great course

автор: yuri f

May 15, 2017

great course

автор: clyce

Nov 27, 2016

Nice course.

автор: Pedro R

Nov 09, 2016

great course

автор: Frank

Dec 15, 2017

老师太天马行空了。。。

автор: HOLLY W

May 25, 2019

课程特别好,资料丰富

автор: Siyeong L

Jan 22, 2017

Awesome!!!

автор: Alireza N

Jan 12, 2017

Excellent!

автор: dingjingtao

Jan 07, 2017

excellent!

автор: Phan T B

Dec 02, 2016

very good!

автор: Jax

Jan 09, 2017

very nice

автор: Jose A A S

Nov 25, 2016

Wonderful

автор: mohammed o

Oct 18, 2016

Fantastic

автор: zhou

Oct 13, 2016

very good

автор: 张浩悦

Nov 22, 2018

funny!!

автор: Alexander A S G

Feb 10, 2017

Thanks

автор: oilover

Dec 03, 2016

老师很棒!!

автор: 刘仕琪

Oct 31, 2016

不错的一门课

автор: Accenture X

Oct 12, 2016

Great

автор: Ludovic P

Oct 29, 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.