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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



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....



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!!


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).


Probabilistic Graphical Models 1: Representation: 151 - 175 / 271 レビュー

by 艾萨克

Nov 07, 2016

useful! A little diffcult

by Souvik C

Oct 26, 2016

Extremely helpful course

by Joris S

Feb 16, 2020

Well presented course!

by Jiew W

Apr 17, 2018

very good, practical.

by Wei C

Mar 06, 2018

good online coursera

by Nijesh U

Jul 18, 2019

Thanks for offering

by Hang D

Oct 09, 2016

really well taught

by Anil K

Oct 30, 2017

Very intuitive...

by Kar T Q

Mar 02, 2017

Excellent course.

by Labmem

Oct 03, 2016

Great Course!!!!!

by Phung H X

Oct 30, 2016

very good course

by Logé F

Nov 19, 2017

Great course !

by Diego T

Jun 09, 2017

Great content!

by Yue S

May 09, 2019

Great course!

by David D

May 30, 2017

Mind blowing!

by Yang P

Apr 26, 2017

Great course.

by Nairouz M

Feb 14, 2017

Very helpful.

by brotherzhao

Feb 15, 2020

nice course!

by Utkarsh A

Dec 30, 2018

maza aa gaya

by Musalula S

Aug 02, 2018

Great course

by yuri f

May 15, 2017

great course

by clyce

Nov 27, 2016

Nice course.

by Pedro R

Nov 09, 2016

great course

by Frank

Dec 15, 2017



May 25, 2019