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Learner Reviews & Feedback for Probabilistic Graphical Models 1: Representation by Stanford University

4.6
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1,406 ratings

About the Course

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....

Top reviews

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).

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76 - 100 of 306 Reviews for Probabilistic Graphical Models 1: Representation

By Christopher M P

Jan 16, 2020

Simply excellent. A wonderful course to begin the representation of PGM. Be advised.... this can get quite advanced. It's all about that Bayes, 'bout that Bayes.... no trouble.

By Christopher B

Jul 17, 2017

learned a lot. lectures were easy to follow and the textbook was able to more fully explain things when I needed it. looking forward to the next course in the series.

By 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.

By ChrisLJ

Mar 25, 2020

really great course! very clear and logical structure. I completed a graphical models course as part of my master's degree, and this really helped to consolidate it

By Prasid S

Dec 7, 2016

Very well designed. There were areas here I struggled with the technical details and had to read up a lot to understand. The assignments are very well designed.

By Al F

Mar 19, 2018

Excellent Course. Very Deep Material. I purchased the Text Book to allow for a deeper understanding and it made the course so much easier. Highly recommended

By John P

Jun 16, 2022

A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.

By 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

By Sureerat R

Mar 2, 2018

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

By Angel G G

Dec 12, 2019

Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.

By 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.

By Valeriy Z

Nov 13, 2017

This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.

By Mulang' O

Mar 31, 2019

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

By Singhi K

Aug 1, 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.

By Karam D

Apr 3, 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

By ALBERTO O A

Oct 16, 2018

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

By 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.

By Pathirage D

May 29, 2021

one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.

By Matt M

Oct 22, 2016

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

By Samuel B

Mar 13, 2021

Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.

By Anton K

May 7, 2018

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

By Kelvin L

Aug 11, 2017

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

By 杨涛

Mar 27, 2019

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

By HARDIAN L

Jun 23, 2018

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

By Satish P

Jul 12, 2020

A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.