Retour à Probabilistic Graphical Models 1: Representation

4.7

1,126 notes

•

246 avis

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....

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!!

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).

Filtrer par :

par Vivek G

•Apr 27, 2019

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

par Isaiah O M

•Mar 31, 2019

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

par 杨涛

•Mar 27, 2019

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

par HOLLY W

•May 25, 2019

课程特别好，资料丰富

par Jui-wen L

•Jun 21, 2019

Easy to follow and very informative.

par Nijesh

•Jul 18, 2019

Thanks for offering

par Anthony L

•Jul 20, 2019

Some parts are challenging enough in the PAs, if you are familiar with Matlab this course is a great opportunity to get familiar with PGMs and learn to handle these.

par Harshdeep S

•Jul 19, 2019

Excellent blend of maths & intuition.

par Mike P

•Jul 30, 2019

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

par Parag H S

•Aug 14, 2019

Learn the basic things in probability theory

par 郭玮

•Apr 26, 2019

Really nice course, thank you!

par Yue S

•May 09, 2019

Great course!

par Ayush T

•Aug 23, 2019

This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

par Meysam G

•Sep 12, 2019

I had actually read the David Barber book before I took this course. The course provides a deep insight to the PGMs which is necessary if one wants to utilize it in real applications or as in my case in research works. Moreover, the language of the instructor is comfortably plain, especially when it comes to explaining somewhat complicated concepts. In general, it is highly recommended.

par hyesung J

•Oct 10, 2019

So difficult. But interesting

par CIST N

•Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

par Pouya E

•Oct 13, 2019

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

par Xiaojie Z

•Dec 22, 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

par Myoungsu C

•Dec 26, 2018

Writing on the ppt is not clear to see.

par Sunsik K

•Jul 31, 2018

Broad introduction to general issues

par Shantanu B

•Sep 03, 2018

This course is a very essential learning step for people who want to learn and work with Baysean or Markov networks. I think that the course can be further improved by going a little slow on certain assertions or deductions which are fundamental to the subject. Those should be properly emphasized. But overall the assignments were challenging and actually made you think about the things taught in that corresponding video.

par Alain M

•Nov 03, 2018

Overall very good quality content. PAs are useful but some questions/tests leave too much to interpretation and can be frustrating for students. Audio quality for the classes could also be improved.

par Sunil

•Sep 12, 2017

Great intro to probabilistic models

par Péter D

•Oct 29, 2017

great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

par george v

•Jul 07, 2017

very nice intuition from the professor Daphne Koller and "compact" in these lectures that dont exceed 15min each. really glad i did the first one, wish i did also the other two parts, certainly will when i find the time. Just as a comment, i mostly enjoyed the programming assignments. they are very well structured and in a very particular manner, which at the same time is the strong and the weak point of the assingment, since at times i undertsood something else than what the actual implementation was. anyway they were really a challenge, and whoever manages to do them should be glad with his work. Thank you prof. Koller for this course!

Coursera propose un accès universel à la meilleure formation au monde,
en partenariat avec des universités et des organisations du plus haut niveau, pour proposer des cours en ligne.