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Avis et commentaires pour l'étudiant pour Probabilistic Graphical Models 1: Representation par Université de Stanford

4.7
1,125 notes
246 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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76 - 100 sur 239 Examens pour Probabilistic Graphical Models 1: Representation

par Souvik C

Oct 26, 2016

Extremely helpful course

par Achen

May 06, 2018

a bit too hard if you don't have enough probability knowledge

par George S

Jun 18, 2017

Excellent material presentation

par Naveen M N S

Dec 13, 2016

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

par Wei C

Mar 06, 2018

good online coursera

par Logé F

Nov 19, 2017

Great course !

par Elvis S

Oct 29, 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

par Stephen F

Feb 26, 2017

This is a course for those interested in advancing probabilistic modeling and computation.

par Mohammd K D

Apr 03, 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

par Shengliang

May 29, 2017

excellent explanations! Thanks professor!

par Abhishek K

Nov 13, 2016

Superb exposition. Makes me want to continue learning till the very end of this course. Very intuitive explanations. Plan to complete all courses offered in this specialization.

par Gary H

Mar 28, 2018

Great instructor and information.

par David C

Nov 01, 2016

If you are interested in graphical models, you should take this course.

par Chatard J

Nov 25, 2016

Une méthode pédagogique sans faille. Des contrôles et des exercices qui permettent d'approfondir ce qu'on apprend et de faire le point en permanence. Un merveilleux voyage dans le monde des Modèles Graphiques Probabilistes.

par Siyeong L

Jan 22, 2017

Awesome!!!

par Kelvin L

Aug 11, 2017

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

par Yang P

Apr 26, 2017

Great course.

par Nairouz M

Feb 14, 2017

Very helpful.

par Douglas G

Oct 24, 2016

This course is very help for who have to study anything the respect of machine learning example, which is a thing much used in every day and in the new context of new industries 4.0, and the studies of probabilistcs graphical can help who need to develop new programs each times more efectiviness and best.

par Justin C

Oct 23, 2016

This was a fantastic introduction to PGM for a non-expert. It is well paced for an online course and the assignments provide enough depth to hone your knowledge and skills within the 5 week timeframe. Highly recommended.

par Dawood A C

Oct 25, 2016

The course was very fruitful. It is was not that easy of course, I think it is one of the most difficult courses on Coursera but it deserves to try it once, twice and as many as you can until you understand the idea behind the course. The exams and the honor assignments were so tricky and not that easy to solve. If you don't have a probabilistic background, I think first better for you to take a course in data analysis and probability.

par 庭緯 任

Jan 10, 2017

perfect lesson!! Although the course is hard, the professor teaches very well!!

par dingjingtao

Jan 07, 2017

excellent!

par clyce

Nov 27, 2016

Nice course.

par HARDIAN L

Jun 23, 2018

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