Chevron Left
Voltar para Probabilistic Graphical Models 1: Representation

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

4.6
estrelas
1,385 classificações

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).

Filtrar por:

176 — 200 de 301 Avaliações para o Probabilistic Graphical Models 1: Representation

por phung h x

30 de out de 2016

por Frédéric L M

19 de nov de 2017

por Diego T

9 de jun de 2017

por Yue S

9 de mai de 2019

por David D

30 de mai de 2017

por Yang P

26 de abr de 2017

por Nairouz M

13 de fev de 2017

por brotherzhao

15 de fev de 2020

por Utkarsh A

30 de dez de 2018

por Musalula S

2 de ago de 2018

por Yuri F

15 de mai de 2017

por 赵紫川

27 de nov de 2016

por Pedro R

9 de nov de 2016

por Frank

14 de dez de 2017

por HOLLY W

24 de mai de 2019

por Siyeong L

21 de jan de 2017

por Alireza N

12 de jan de 2017

por dingjingtao

7 de jan de 2017

por Phan T B

2 de dez de 2016

por Jax

8 de jan de 2017

por Jose A A S

25 de nov de 2016

por mohammed o

18 de out de 2016

por zhou

13 de out de 2016

por 张浩悦

22 de nov de 2018

por Alexander A S G

9 de fev de 2017