返回到 Probabilistic Graphical Models 1: Representation

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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).

篩選依據：

創建者 Nairouz M

•Feb 14, 2017

Very helpful.

創建者 Douglas G

•Oct 24, 2016

This course is very help for who have to study anything the respect of machine learning example, which is a thing much used in every day and in the new context of new industries 4.0, and the studies of probabilistcs graphical can help who need to develop new programs each times more efectiviness and best.

創建者 Justin C

•Oct 23, 2016

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

創建者 庭緯 任

•Jan 10, 2017

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

創建者 clyce

•Nov 27, 2016

Nice course.

創建者 HARDIAN L

•Jun 23, 2018

Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.

創建者 Jinsun P

•Jan 17, 2017

Really Helpful for Studying!

創建者 Arthur C

•Jun 04, 2017

Super useful if you want to understand any probability model.

創建者 Sriram P

•Jun 24, 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.

創建者 Labmem

•Oct 03, 2016

Great Course!!!!!

創建者 Accenture X

•Oct 12, 2016

Great

創建者 Saikat M

•Aug 01, 2017

Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.

創建者 zhou

•Oct 13, 2016

very good

創建者 Minh N

•Mar 01, 2017

Quite a steep learning curve. Definitely not for those without prior experience in machine learning, or statistics in general. Also, I would much appreciate it if more test cases were provided in the programming assignments to help with debugging.

創建者 Gautam B

•Jul 04, 2017

Great course loved the ongoing feedback when doing the quizes.

創建者 YUXUN L

•Dec 07, 2016

This course is really amazing. The lecture is well-organised and lecture material is good. This course covers basic knowledge about representation in Probabilistic Graphical Model. It includes Markov Network, Bayesian Network, Template Model and some other knowledge. The assignments, oh, I have to say, although some quiz in it seems like having bug, are still impressive. I strongly recommend finishing all the programming assignments of this course. Some trick parts of the knowledge taught in the course are covered by the assignments (like template model part, trust me you have to think about the template model part really, really carefully to figure out what it exactly means). Anyway, it worth my payment :-).

If you wanna take this course, buying a textbook is a good choice because there are some extra knowledge which is not covered by this course in the textbook. However, without a textbook you can still continue. I really appreciate Professor Koller for offering such a great, amazing course!

創建者 艾萨克

•Nov 07, 2016

useful！ A little diffcult

創建者 Siwei G

•Jun 07, 2017

It's a great class. A lot people may complain that there should be more details. Well, this course may not hold your hands all the way to the end, but it covers enough to get you started to learn independently. It is a graduate level class, and it should be designed in this way. 5 star for the wonderful content.

創建者 Hang D

•Oct 09, 2016

really well taught

創建者 Frank

•Dec 15, 2017

老师太天马行空了。。。

創建者 Prazzy S

•Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

創建者 Matt M

•Oct 22, 2016

Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.

創建者 chen h

•Jan 21, 2018

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

創建者 Ning L

•Oct 18, 2016

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

創建者 roi s

•Oct 29, 2017

I really like how Dafna is teaching the course, very clear!

It will be nice if their could be a following course that will show new frameworks and code that implements PGMs. Like the courses of deep learning where Andrew Ng is focusing mostly on the practical side.