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
1,402 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:

101 — 125 de 304 Avaliações para o Probabilistic Graphical Models 1: Representation

por Johannes C

19 de abr de 2020

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

por Alexandru I

25 de nov de 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

por Rajmadhan E

7 de ago de 2017

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

por Lucian

15 de jan de 2017

Some more exam questions and variation, including explanations when failing, would be very useful.

por Onur B

13 de nov de 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

por Elvis S

28 de out de 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

por Youwei Z

19 de mai de 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

por Umais Z

23 de ago de 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

por Hao G

1 de nov de 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

por Alfred D

2 de jul de 2020

Was a little difficult in the middle but the last section summary just refreshed all of it

por Stephen F

26 de fev de 2017

This is a course for those interested in advancing probabilistic modeling and computation.

por Una S

24 de jul de 2020

Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

por liang c

15 de nov de 2016

Great course. and it is really a good chance to study it well under Koller's instruction.

por AlexanderV

9 de mar de 2020

Great course, except that the programming assignments are in Matlab rather than Python

por Ning L

17 de out de 2016

This is a very good course for the foundation knowledge for AI related technologies.

por Hong F

21 de jun de 2020

Hope there are explanations of the hard questions (marked by *) in the final exam.

por Abhishek K

6 de nov de 2016

Difficult yet very good to understand even after knowing about ML for a long time.

por chen h

20 de jan de 2018

The exercise is a little difficult. Need to revise several times to fully digest.

por Isaac A

23 de mar de 2017

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

por 庭緯 任

10 de jan de 2017

perfect lesson!! Although the course is hard, the professor teaches very well!!

por Alejandro D P

29 de jun de 2018

This and its sequels, the most interesting Coursera courses I've taken so far.

por Naveen M N S

13 de dez de 2016

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

por Amritesh T

25 de nov de 2016

highly recommended if you wanna learn the basics of ML before getting into it.

por Pouya E

13 de out de 2019

Well-structured content, engaging programming assignments in honors track.

por David C

1 de nov de 2016

If you are interested in graphical models, you should take this course.