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Retour à Probabilistic Graphical Models 1: Representation

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

1,320 évaluations
294 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

12 juil. 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!!

22 oct. 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).

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151 - 175 sur 287 Avis pour Probabilistic Graphical Models 1: Representation

par 郭玮

25 avr. 2019

Really nice course, thank you!

par hyesung J

10 oct. 2019

So difficult. But interesting

par Jinsun P

16 janv. 2017

Really Helpful for Studying!

par Shengding H

10 mars 2019

A very nice-designed course

par Marno B

3 févr. 2019

Absolutely love it!!!!


par Nguyễn L T Â

5 févr. 2018

Thank you, the professor.

par hy395

13 sept. 2017

Very clear and intuitive.

par 艾萨克

6 nov. 2016

useful! A little diffcult

par Souvik C

26 oct. 2016

Extremely helpful course

par Joris S

16 févr. 2020

Well presented course!

par Jiew W

17 avr. 2018

very good, practical.

par Wei C

6 mars 2018

good online coursera

par Nijesh U

18 juil. 2019

Thanks for offering

par Hang D

9 oct. 2016

really well taught

par Anil K

30 oct. 2017

Very intuitive...

par Kar T Q

2 mars 2017

Excellent course.

par Labmem

3 oct. 2016

Great Course!!!!!

par Phung H X

30 oct. 2016

very good course

par Logé F

19 nov. 2017

Great course !

par Diego T

9 juin 2017

Great content!

par Yue S

9 mai 2019

Great course!

par David D

30 mai 2017

Mind blowing!

par Yang P

26 avr. 2017

Great course.

par Nairouz M

13 févr. 2017

Very helpful.

par brotherzhao

15 févr. 2020

nice course!