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Voltar para Probabilistic Graphical Models 1: Representation

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

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1,214 classificações
265 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

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).

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151 — 175 de {totalReviews} Avaliações para o Probabilistic Graphical Models 1: Representation

por Hang D

Oct 09, 2016

really well taught

por Anil K

Oct 30, 2017

Very intuitive...

por Kar T Q

Mar 02, 2017

Excellent course.

por Labmem

Oct 03, 2016

Great Course!!!!!

por Phung H X

Oct 30, 2016

very good course

por Logé F

Nov 19, 2017

Great course !

por Diego T

Jun 09, 2017

Great content!

por Yue S

May 09, 2019

Great course!

por David D

May 30, 2017

Mind blowing!

por Yang P

Apr 26, 2017

Great course.

por Nairouz M

Feb 14, 2017

Very helpful.

por brotherzhao

Feb 15, 2020

nice course!

por Utkarsh A

Dec 30, 2018

maza aa gaya

por Musalula S

Aug 02, 2018

Great course

por yuri f

May 15, 2017

great course

por clyce

Nov 27, 2016

Nice course.

por Pedro R

Nov 09, 2016

great course

por Frank

Dec 15, 2017

老师太天马行空了。。。

por HOLLY W

May 25, 2019

课程特别好,资料丰富

por Siyeong L

Jan 22, 2017

Awesome!!!

por Alireza N

Jan 12, 2017

Excellent!

por dingjingtao

Jan 07, 2017

excellent!

por Phan T B

Dec 02, 2016

very good!

por Jax

Jan 09, 2017

very nice

por Jose A A S

Nov 25, 2016

Wonderful