Chevron Left
Probabilistic Graphical Models 1: Representation に戻る

スタンフォード大学（Stanford University） による Probabilistic Graphical Models 1: Representation の受講者のレビューおよびフィードバック

4.6
星
1,375件の評価
•
306件のレビュー

コースについて

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....

人気のレビュー

ST

2017年7月12日

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

2017年10月22日

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).

フィルター：

Probabilistic Graphical Models 1: Representation： 151 - 175 / 299 レビュー

by Harshdeep S

•

2019年7月19日

Excellent blend of maths & intuition.

by NARENDRAN

•

2020年3月7日

Very good explanation on the subject

by Jui-wen L

•

2019年6月20日

Easy to follow and very informative.

by Miriam F

•

2017年8月27日

Very nice and well prepared course!

by Gary H

•

2018年3月27日

Great instructor and information.

by Subham S

•

2020年4月28日

I enjoyed the course very much!

by George S

•

2017年6月18日

Excellent material presentation

by 郭玮

•

2019年4月25日

Really nice course, thank you!

by hyesung J

•

2019年10月10日

So difficult. But interesting

by Jinsun P

•

2017年1月16日

Really Helpful for Studying!

by Shengding H

•

2019年3月10日

A very nice-designed course

by Marno B

•

2019年2月3日

Absolutely love it!!!!

:)

by An N

•

2018年2月5日

Thank you, the professor.

by hy395

•

2017年9月13日

Very clear and intuitive.

by 艾萨克

•

2016年11月6日

useful！ A little diffcult

by Souvik C

•

2016年10月26日

Extremely helpful course

by Joris S

•

2020年2月16日

Well presented course!

by Jiew W

•

2018年4月17日

very good, practical.

by Wei C

•

2018年3月6日

good online coursera

by Nijesh U

•

2019年7月18日

Thanks for offering

by Hang D

•

2016年10月9日

really well taught

by Anil K

•

2017年10月30日

Very intuitive...

by Kar T Q

•

2017年3月2日

Excellent course.

by Labmem

•

2016年10月3日

Great Course!!!!!

by phung h x

•

2016年10月30日

very good course

Coursera Footer

キャリアをスタート、またはキャリアアップする

人気のあるトピックを閲覧

人気のコースと記事

オンラインで学位または証明書を取得する

Coursera

コミュニティ

さらに表示

場所を選ばす学習する
App StoreからダウンロードGoogle Playで取得
Placeholder