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Вернуться к Probabilistic Graphical Models 2: Inference

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

4.6
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Оценки: 411
Рецензии: 59

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....

Лучшие рецензии

AT

Aug 23, 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.

AL

Aug 20, 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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51–59 из 59 отзывов о курсе Probabilistic Graphical Models 2: Inference

автор: Siwei Y

Jan 17, 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。

автор: Chris V

Dec 14, 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

автор: Tomer N

Jun 20, 2018

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

автор: Thomas W

May 05, 2017

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

автор: fan

Nov 20, 2016

Can't get score for free!!!

автор: Jiaxing L

Nov 27, 2016

I am kind of disappointed that you have to pay for the course before you can submit the solution to the problem set. However, that is not the main issue of this course, as I fully understand that the financial profit for the lecturer is very important. The main issue of this course is that the chaos in the symbol used in the second programming assignment, the lecturer cannot even main self-consistency in the symbol used. The statement of everything in both PA1 and PA2 is also very confusing.

автор: Hunter J

May 02, 2017

The lectures are fine and the book is great, but the assignments have a lot of technical problems. I spent most of my effort trying to solve trivial issues with the sample code and dealing with the auto grader.

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: 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!