Chevron Left
Retour à Probabilistic Graphical Models 1: Representation

Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 1: Representation par Université de Stanford

4.7
étoiles
1,193 évaluations
259 avis

À propos du cours

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....

Meilleurs avis

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).

Filtrer par :

126 - 150 sur 252 Avis pour Probabilistic Graphical Models 1: Representation

par Jui-wen L

Jun 21, 2019

Easy to follow and very informative.

par Miriam F

Aug 27, 2017

Very nice and well prepared course!

par Gary H

Mar 28, 2018

Great instructor and information.

par George S

Jun 18, 2017

Excellent material presentation

par 郭玮

Apr 26, 2019

Really nice course, thank you!

par hyesung J

Oct 10, 2019

So difficult. But interesting

par Jinsun P

Jan 17, 2017

Really Helpful for Studying!

par Shengding H

Mar 10, 2019

A very nice-designed course

par Marno B

Feb 03, 2019

Absolutely love it!!!!

:)

par Nguyễn L T Â

Feb 06, 2018

Thank you, the professor.

par hy395

Sep 13, 2017

Very clear and intuitive.

par 艾萨克

Nov 07, 2016

useful! A little diffcult

par Souvik C

Oct 26, 2016

Extremely helpful course

par Joris S

Feb 16, 2020

Well presented course!

par Jiew W

Apr 17, 2018

very good, practical.

par Wei C

Mar 06, 2018

good online coursera

par Nijesh

Jul 18, 2019

Thanks for offering

par Hang D

Oct 09, 2016

really well taught

par Anil K

Oct 30, 2017

Very intuitive...

par Kar T Q

Mar 02, 2017

Excellent course.

par Labmem

Oct 03, 2016

Great Course!!!!!

par Phung H X

Oct 30, 2016

very good course

par Logé F

Nov 19, 2017

Great course !

par Diego T

Jun 09, 2017

Great content!

par Yue S

May 09, 2019

Great course!