Back to Probabilistic Graphical Models 1: Representation

4.7

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1,274 ratings

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281 reviews

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).

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By 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!

By Laurent G

•May 05, 2020

This is overall a great course. It required me a bit of reading outside of the course material, and fail on quizzes a few times before understanding, but it is was very much worth the effort. However, the assignments in MATLAB and IAMSAM feel dated. As much as I would like to exercise the newly acquired knowledge with exercises MATLAB is particularly irritating after having used other languages.

By Zhen L

•Nov 16, 2016

The course gives an good introduction of PGM. The highlights are the well-designed quizzes and assignments. But the videos of lectures are not good enough. It's too fast and some key concepts are not clearly explained.

After looked into another course on coursera, I add a star for this....

By Vincent L

•Mar 21, 2018

Some of the examples are a bit confusing. I mostly used logic to solve these versus following a formula. Octave was fine but I didn't know how to use SAMIAM and so gave up on the coding assignments since PGMs aren't a focus area for me except for general theoretical knowledge.

By Roland R

•Dec 20, 2017

Good course. Sometimes a little bit hard to follow. For example representation of probability functions as graphs (connection between factorisation of probabilty distribution and cliques in the graph). And I'm not sure If I can apply PGMs to real world problems now.

By Hanbo L

•Apr 30, 2017

In general this is a good introductory course. You should read the book if you want more in-depth knowledge in this field. I feel that some of the concepts can be expanded a little more, like local structure in Markov model. Overall, this is a great course.

By 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.

By 邓成标

•Nov 30, 2017

The materials are very interesting, however, this professor speaks so fast that it is hard to grasp the deep theory. In overall, this course is great. And I really need to do the assignment to enhance my comprehension about the content.

By Surender K

•Nov 07, 2016

Wonderful course with great material. Wish there were more examples in the material. Nonetheless cannot complain to get this course for free with SEE material and programming assignments (need to complete yet in this session)

By Akshaya T

•Jan 16, 2018

Some tutorials need disambiguating documentation (upgrade :)) but otherwise, the course is really good. It would also help if there is a mention of what chapters to study from the book for every lesson -- in the slides.

By Rajeev B A

•Dec 23, 2017

This specialization covers a lot of concepts and programming assignments which are very helpful in understanding the concepts clearly. Although, I wish there is some form of explanation for the programming assignments.

By 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.

By 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.

By Shawn C

•Nov 05, 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.

By 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

By 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..

By 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.

By 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.

By Jack A

•Nov 05, 2017

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

By François 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)

By 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.

By Ashwin P

•Jan 09, 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.

By Forest R

•Feb 20, 2018

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

By Ivan D M

•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.

By Xiaojie Z

•Dec 22, 2018

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

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