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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

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1,356件の評価
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302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： 126 - 150 / 295 レビュー

by Arthur C

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2017年6月4日

Super useful if you want to understand any probability model.

by Ruiliang L

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2021年2月15日

Awesome class to gain solid understanding of graphical model

by Vu P

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2020年3月18日

Great course, learned a lots. Thanks professor Daphne Koller

by Sriram P

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2017年6月24日

Had a wonderful learning experience, Thank You Daphne Ma'am.

by Pablo G M D

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2018年7月18日

Outstanding teaching and the assignments are quite useful!

by Ziheng

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2016年11月14日

Very informative course, and incredibly useful in research

by Ingyo C

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2018年10月4日

What a wonderful course that I haven't ever taken before.

by Renjith K A

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2018年9月23日

Was really helpful in understanding graphic models

by Roger T

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2017年3月5日

very challenging class but very rewarding as well!

by Harshit A

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2021年4月20日

This is a challenging but very satisfying course.

by 吕野

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2016年12月26日

Good course lectures and programming assignments

by Mahmoud S

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2019年2月25日

Very good explanation and excellent assignments

by Lilli B

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2018年2月2日

Brilliant content and charismatic lecturer!!!

by Fabio S

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2017年9月25日

Excellent, well structured, clear and concise

by llv23

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2017年7月19日

Very good and excellent course and assignment

by Parag H S

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2019年8月14日

Learn the basic things in probability theory

by Christian S

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2020年12月11日

Highest level in coursera courses so far.

by Jonathan H

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2017年11月25日

This course is hard and very interesting!

by Shengliang X

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2017年5月29日

excellent explanations! Thanks professor!

by Alexander K

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2017年5月16日

Thank you for all. This is gift for us.

by Chahat C

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2019年5月4日

lectures not good(i mean not detailed)

by Harshdeep S

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2019年7月19日

Excellent blend of maths & intuition.

by NARENDRAN

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2020年3月7日

Very good explanation on the subject

by Jui-wen L

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2019年6月20日

Easy to follow and very informative.

by Miriam F

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2017年8月27日

Very nice and well prepared course!

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