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Comentários e feedback de alunos de Probabilistic Graphical Models 1: Representation da instituição Universidade de Stanford

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304 avaliações

Sobre o curso

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....

Melhores avaliações

ST
12 de Jul de 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
22 de Out de 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 de 297 Avaliações para o Probabilistic Graphical Models 1: Representation

por Gary H

27 de Mar de 2018

Great instructor and information.

por Subham S

28 de Abr de 2020

I enjoyed the course very much!

por George S

18 de Jun de 2017

Excellent material presentation

por 郭玮

25 de Abr de 2019

Really nice course, thank you!

por hyesung J

10 de Out de 2019

So difficult. But interesting

por Jinsun P

16 de Jan de 2017

Really Helpful for Studying!

por Shengding H

10 de Mar de 2019

A very nice-designed course

por Marno B

3 de Fev de 2019

Absolutely love it!!!!

:)

por An N

5 de Fev de 2018

Thank you, the professor.

por hy395

13 de Set de 2017

Very clear and intuitive.

por 艾萨克

6 de Nov de 2016

useful! A little diffcult

por Souvik C

26 de Out de 2016

Extremely helpful course

por Joris S

16 de Fev de 2020

Well presented course!

por Jiew W

17 de Abr de 2018

very good, practical.

por Wei C

6 de Mar de 2018

good online coursera

por Nijesh U

18 de Jul de 2019

Thanks for offering

por Hang D

9 de Out de 2016

really well taught

por Anil K

30 de Out de 2017

Very intuitive...

por Kar T Q

2 de Mar de 2017

Excellent course.

por Labmem

3 de Out de 2016

Great Course!!!!!

por phung h x

30 de Out de 2016

very good course

por Frédéric L M

19 de Nov de 2017

Great course !

por Diego T

9 de Jun de 2017

Great content!

por Yue S

9 de Mai de 2019

Great course!

por David D

30 de Mai de 2017

Mind blowing!