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: Alexey K

•Nov 17, 2017

Thank you! It's simply incredible exercise for brain! :-) The best ever course here, which teaches one to really think and model, rather than merely click to choose most plausible answer ( like other courses do )

교육 기관: Eric S

•Feb 01, 2018

A very in depth course on PGNs. You definitely need some background in math and a willingness to invest a lot of time into the course. Of most value to me were the programming exercises. They are in Octave as this is one of the earliest Coursera courses, but it is worth exploring the provided implementations.

교육 기관: Blake B

•May 21, 2017

Awesome intro to graphical models, and the exercises really emphasize understanding and proceed at what seems like the appropriate pace. Challenging for sure, you need to want to learn this stuff. Only downside is I'm not a fan of using octave/matlab--really wish this could be rebuilt using python for all the exercises. I've probably spent 60% of my time devoted to this course on getting that setup working and wrestling with telling the computer to do what I want in an unpopular language--at least, unpopular out in the world outside of academia.

교육 기관: Sha L

•Apr 20, 2017

it's really hard course for me but after completing and see the certificate I feel so good about it. Yesterday someone asked a question regarding conditional independence. I remember before I took the course I've spent quite some time understanding it, just like him. But yesterday I didn't event think about it and gave him the right answer using "active trail" and "D-separation" concept. That's how powerful this course can be.

I didn't work on the honor track though because I'm currently short of time. But I think I will come back and taking the other 2 courses in this series.

교육 기관: Ofelia P R P

•Dec 11, 2017

Curso muy completo que da conocimiento realmente avanzado sobre modelos gráficos probabilísticos. Aviso, la especialización es complicada para los que no somos expertos del tema!

교육 기관: llv23

•Jul 19, 2017

Very good and excellent course and assignment

교육 기관: 王文君

•May 21, 2017

Awesome class, the content is not too easy as most online courses. Still the instructor states the concepts clearly and the assignments aligns very well with the content to help me deepen my understanding of the concepts. The assignments are meaningful and challenging, finishing them gave me a great sense of achievement!!

It would be better if the examples in the classes could incorporate some industry applications.

교육 기관: Pedro R

•Nov 09, 2016

great course

교육 기관: Phan T B

•Dec 02, 2016

very good!

교육 기관: Ziheng

•Nov 14, 2016

Very informative course, and incredibly useful in research

교육 기관: Elvis S

•Oct 29, 2016

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

교육 기관: Stephen F

•Feb 26, 2017

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

교육 기관: Hao G

•Nov 01, 2016

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

교육 기관: Mohammd K D

•Apr 03, 2017

One of the best courses which i visited.

The explanation was so simple and there were many examples which were so helpful for me

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교육 기관: Shengliang

•May 29, 2017

excellent explanations! Thanks professor!

교육 기관: Achen

•May 06, 2018

a bit too hard if you don't have enough probability knowledge

교육 기관: George S

•Jun 18, 2017

Excellent material presentation

교육 기관: David C

•Nov 01, 2016

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

교육 기관: Chuck M

•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).

교육 기관: Phung H X

•Oct 30, 2016

very good course

교육 기관: Alireza N

•Jan 12, 2017

Excellent!

교육 기관: Fabio S

•Sep 25, 2017

Excellent, well structured, clear and concise

교육 기관: Siyeong L

•Jan 22, 2017

Awesome!!!

교육 기관: Kelvin L

•Aug 11, 2017

I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!

교육 기관: Yang P

•Apr 26, 2017

Great course.