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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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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 304 Avaliações para o Probabilistic Graphical Models 1: Representation

por Fabio S

25 de set de 2017

Excellent, well structured, clear and concise

por Orlando D

19 de jul de 2017

Very good and excellent course and assignment

por Parag S

14 de ago de 2019

Learn the basic things in probability theory

por Christian S

11 de dez de 2020

Highest level in coursera courses so far.

por Jonathan H

25 de nov de 2017

This course is hard and very interesting!

por Shengliang X

29 de mai de 2017

excellent explanations! Thanks professor!

por Alexander K

16 de mai de 2017

Thank you for all. This is gift for us.

por Chahat C

4 de mai de 2019

lectures not good(i mean not detailed)

por Harshdeep S

19 de jul de 2019

Excellent blend of maths & intuition.

por NARENDRAN

7 de mar de 2020

Very good explanation on the subject

por Jui-wen L

20 de jun de 2019

Easy to follow and very informative.

por Miriam F

27 de ago de 2017

Very nice and well prepared course!

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 지혜성

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!