Probabilistic Graphical Models 1: Representation(으)로 돌아가기

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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).

필터링 기준:

교육 기관: Yue S

•May 09, 2019

Great course!

교육 기관: 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

교육 기관: 郭玮

•Apr 26, 2019

Really nice course, thank you!

교육 기관: Chahat C

•May 04, 2019

lectures not good(i mean not detailed)

교육 기관: Sumod K M

•May 06, 2019

The course contents and presentation is of very high quality. The assignments and quizzes are both challenging and very rewarding. The only minor qualm is that the programming assignment grader seems to have few issues. For one, MATLAB indexing is really hard to work with. Secondly, it doesn't test the answers fully in some cases. Like the case of OptimizeWithJointUtility, OptimizeLinearExpectations. My codes passed the grader but I was splitting to hair to figure out why my answers to quiz questions corresponding to programming assignment were wrong. Turned out that my code was incorrect for the two programming assignments and that was causing issues. Otherwise, really nice course. Thank you :).

교육 기관: Jui-wen L

•Jun 21, 2019

Easy to follow and very informative.

교육 기관: HOLLY W

•May 25, 2019

课程特别好，资料丰富

교육 기관: Nijesh

•Jul 18, 2019

Thanks for offering

교육 기관: Harshdeep S

•Jul 19, 2019

Excellent blend of maths & intuition.

교육 기관: 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.

교육 기관: 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.

교육 기관: Parag H S

•Aug 14, 2019

Learn the basic things in probability theory

교육 기관: 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.

교육 기관: 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.

교육 기관: Xiaojie Z

•Dec 22, 2018

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

교육 기관: Myoungsu C

•Dec 26, 2018

Writing on the ppt is not clear to see.

교육 기관: Mehmet U

•Jul 02, 2017

Thanks for offering this course, I have learned a lot. However, the course is quite confusing. Not everything is well defined so it is hard to answer some questions. The honors programming assignments are usually confusing in this manner. If you put in the effort to understand it thou, it can be done. To be honest thou, some misunderstanding could be given to my lack of understanding the material at first. At the same time my lack of understanding is probably caused by the course material being not so well defined. Maybe it would help if one spends more time reading the text book.

교육 기관: Rick d W

•Apr 20, 2017

Everything is explained very clearly throughout the course, and the structure they use to teach the subject , from basics to advanced material, is especially helpful. Would recommend this course to anyone with an interest in probabilistic modelling.

교육 기관: Gorazd H R

•Jul 07, 2018

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

교육 기관: mathieu.zaradzki@gmail.com

•Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

교육 기관: Luiz C

•Jun 26, 2018

Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory

교육 기관: Michael K

•Nov 14, 2016

This excellent course is exceptional in that very few MOOCs are taught at this graduate level. Others have pointed out that while this is an introductory course to Probability Graphical Models, I would say that this is still an advanced course, with lots of prerequisites. Prof. Koller is an excellent lecturer, yet moves fast, and you'll need to do reading to fill in the gaps. I haven't been able to find a good book to accompany the course, as her book is pretty dry. I strongly recommend one complete all of the Honors assignments to get a lot out of the course. The discussion boards are not so active with plenty of unanswered questions. Doing the programming assignments will greatly enhance your skills in debugging.

교육 기관: Alberto C

•Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

교육 기관: Caio A M M

•Dec 03, 2016

Instructor is engaging in her delivery. Topic is interesting but difficult.

교육 기관: 李俊宏

•Nov 09, 2017

This is a tough course so it was split into 3 parts. I've learned some ideas about bayesian network and markov model. The major problem about this course is the programming assignment, which is poorly maintained. Daphne Koller is very brilliant but this makes it hard for people to catch up with her, especially for people whose mother language is not English. After all, this is an interesting course!