AM
4.0Reviewed Nov 2, 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.
Probabilistic Graphical Models 1: Representation
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.
Status: Network Model
Status: Decision Intelligence
AdvancedCourse67 hours
Featured reviews
AS
4.0Reviewed Sep 7, 2023
Everything is fine except the bugs in programming assignments. Although it says advance course, the programming assignments aren't that hard. The problems is difficult to submit it to Coursera.
CB
5.0Reviewed Jul 16, 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.
AF
5.0Reviewed 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
CM
5.0Reviewed 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).
RG
5.0Reviewed 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!!
AL
5.0Reviewed Jul 19, 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.
JP
5.0Reviewed Jun 15, 2022
A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.
SR
5.0Reviewed Mar 1, 2018
This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.
DP
5.0Reviewed Jun 17, 2017
I have Actually Earned Three Years of my life (at least) and one possible patent because of this course.Thank You Daphne Mam. God Bless Everybody Associated with it.
PS
5.0Reviewed 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.
AB
5.0Reviewed Aug 30, 2018
Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.