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Avis et commentaires pour d'étudiants pour Probabilistic Graphical Models 1: Representation par Université de Stanford

4.7
étoiles
1,274 évaluations
281 avis

À propos du cours

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....

Meilleurs avis

ST

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!!

CM

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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226 - 250 sur 275 Avis pour Probabilistic Graphical Models 1: Representation

par Luiz C

Jun 26, 2018

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

par Saurabh N

Mar 24, 2020

The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

par Werner N

Dec 28, 2016

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

par Haitham S

Nov 24, 2016

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

par Kevin W

Jan 17, 2017

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

par Péter D

Oct 29, 2017

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

par Andres P N

Jun 27, 2018

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

par Ahmad E

Aug 20, 2017

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

par Soteris S

Nov 27, 2017

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

par mathieu.zaradzki@gmail.com

Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

par Caio A M M

Dec 03, 2016

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

par Michael B

Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

par Anshuman S

May 08, 2019

I would recommend adding some supplemental reading material.

par Jhonatan d S O

May 25, 2017

Rich content and useful tools for applying in real problems

par Vahan A

May 31, 2020

Please, provide programming assignments on Python or C++

par Alberto C

Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

par Yuanduo H

Jan 20, 2020

Five stars minus the week 4 coding homework

par Arthur B

Jan 08, 2017

More feedback from TA would be appreciated

par Myoungsu C

Dec 26, 2018

Writing on the ppt is not clear to see.

par Soumyadipta D

Jul 16, 2019

lectures are too fast otherwise great

par Sunsik K

Jul 31, 2018

Broad introduction to general issues

par Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

par Sunil

Sep 12, 2017

Great intro to probabilistic models

par Nikesh B

Nov 06, 2016

Excellent

par Tianqi Y

Jun 20, 2019

too hard