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....

ST

Jul 12, 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

Oct 22, 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).

筛选依据：

创建者 Boxiao M

•Jun 28, 2017

The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

创建者 Shawn C

•Nov 5, 2016

The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.

创建者 Shane C

•May 18, 2020

concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos

创建者 Hilmi E

•Feb 16, 2020

I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..

创建者 Nimo F B

•Sep 10, 2020

Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines

创建者 Roman S

•Mar 20, 2018

A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.

创建者 Serge S

•Oct 18, 2016

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

创建者 Jack A

•Nov 5, 2017

The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

创建者 Francois L

•Mar 16, 2020

Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)

创建者 Gorazd H R

•Jul 7, 2018

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

创建者 Ashwin P

•Jan 9, 2017

Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

创建者 Forest R

•Feb 20, 2018

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

创建者 Иван М

•Apr 26, 2020

Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.

创建者 Xiaojie Z

•Dec 22, 2018

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

创建者 Luiz C

•Jun 26, 2018

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

创建者 Saurabh N

•Mar 24, 2020

The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

创建者 Werner N

•Dec 28, 2016

Very good course. It should contain more practical examples to make the material better to understand.

创建者 Haitham S

•Nov 24, 2016

Great course, however, the honors track assignments are a bit too tedious and take lots of time.

创建者 Kevin W

•Jan 17, 2017

The course is pretty good. I love the way that the professor led us into the graphical models.

创建者 Péter D

•Oct 29, 2017

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

创建者 Andres P N

•Jun 27, 2018

There are many error in the implementations for octave. Aside from that, the course is fine

创建者 Ahmad E

•Aug 20, 2017

Covers some material a little too quickly, but overall a good and entertaining course.

创建者 Soteris S

•Nov 27, 2017

A bit more challenging than I thought but very useful, and very well structured

创建者 mathieu.zaradzki@gmail.com

•Oct 4, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

创建者 Caio A M M

•Dec 2, 2016

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