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

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307 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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176 — 200 de 300 Avaliações para o Probabilistic Graphical Models 1: Representation

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!

por Yang P

26 de abr de 2017

Great course.

por Nairouz M

13 de fev de 2017

Very helpful.

por brotherzhao

15 de fev de 2020

nice course!

por Utkarsh A

30 de dez de 2018

maza aa gaya

por Musalula S

2 de ago de 2018

Great course

por Yuri F

15 de mai de 2017

great course

por 赵紫川

27 de nov de 2016

Nice course.

por Pedro R

9 de nov de 2016

great course

por Frank

14 de dez de 2017

老师太天马行空了。。。

por HOLLY W

24 de mai de 2019

课程特别好,资料丰富

por Siyeong L

21 de jan de 2017

Awesome!!!

por Alireza N

12 de jan de 2017

Excellent!

por dingjingtao

7 de jan de 2017

excellent!

por Phan T B

2 de dez de 2016

very good!

por Jax

8 de jan de 2017

very nice

por Jose A A S

25 de nov de 2016

Wonderful

por mohammed o

18 de out de 2016

Fantastic

por zhou

13 de out de 2016

very good

por 张浩悦

22 de nov de 2018

funny!!

por Alexander A S G

9 de fev de 2017

Thanks