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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

1,316 évaluations
293 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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126 - 150 sur 286 Avis pour Probabilistic Graphical Models 1: Representation

par Pablo G M D

18 juil. 2018

Outstanding teaching and the assignments are quite useful!

par Ziheng

14 nov. 2016

Very informative course, and incredibly useful in research

par Ingyo C

4 oct. 2018

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

par Renjith K A

23 sept. 2018

Was really helpful in understanding graphic models

par Roger T

5 mars 2017

very challenging class but very rewarding as well!

par 吕野

26 déc. 2016

Good course lectures and programming assignments

par Mahmoud S

25 févr. 2019

Very good explanation and excellent assignments

par Lilli B

2 févr. 2018

Brilliant content and charismatic lecturer!!!

par Fabio S

25 sept. 2017

Excellent, well structured, clear and concise

par llv23

19 juil. 2017

Very good and excellent course and assignment

par Parag H S

14 août 2019

Learn the basic things in probability theory

par Christian S

11 déc. 2020

Highest level in coursera courses so far.

par Jonathan H

25 nov. 2017

This course is hard and very interesting!

par Shengliang X

29 mai 2017

excellent explanations! Thanks professor!

par Alexander K

16 mai 2017

Thank you for all. This is gift for us.

par Chahat C

4 mai 2019

lectures not good(i mean not detailed)

par Harshdeep S

19 juil. 2019

Excellent blend of maths & intuition.


7 mars 2020

Very good explanation on the subject

par Jui-wen L

20 juin 2019

Easy to follow and very informative.

par Miriam F

27 août 2017

Very nice and well prepared course!

par Gary H

27 mars 2018

Great instructor and information.

par Subham S

28 avr. 2020

I enjoyed the course very much!

par George S

18 juin 2017

Excellent material presentation

par 郭玮

25 avr. 2019

Really nice course, thank you!

par hyesung J

10 oct. 2019

So difficult. But interesting