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

4.6
stars
1,424 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

RG

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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301 - 311 of 311 Reviews for Probabilistic Graphical Models 1: Representation

By Siavash R

Aug 10, 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

By Xingjian Z

Nov 2, 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

By Ujjval P

Dec 13, 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

By Jonathan K

Jan 26, 2018

Interesting and useful material, but I found the lecturer unengaging.

By Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

By Robert M

Feb 6, 2018

Started off well. Finished poorly

By Aswin T

Sep 10, 2020

Very rigid questions, very theoretical. Very poor instructor support. Content needs to be improved. Very disconnected approach.

By Deleted A

Jun 11, 2020

very shallow explanation of important concepts

By Shan-Jyun W

Jun 24, 2017

Lectures are awful.

By Belal M

Sep 8, 2017

A very dry course.

By Javier G

Aug 4, 2020

Muy malo