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

4.7
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1,298 évaluations
288 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
12 juil. 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
22 oct. 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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101 - 125 sur 281 Avis pour Probabilistic Graphical Models 1: Representation

par Ning L

17 oct. 2016

This is a very good course for the foundation knowledge for AI related technologies.

par Hong F

21 juin 2020

Hope there are explanations of the hard questions (marked by *) in the final exam.

par Abhishek K

6 nov. 2016

Difficult yet very good to understand even after knowing about ML for a long time.

par chen h

20 janv. 2018

The exercise is a little difficult. Need to revise several times to fully digest.

par Isaac A

23 mars 2017

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

par 庭緯 任

10 janv. 2017

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

par Alejandro D P

29 juin 2018

This and its sequels, the most interesting Coursera courses I've taken so far.

par Naveen M N S

13 déc. 2016

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

par Amritesh T

25 nov. 2016

highly recommended if you wanna learn the basics of ML before getting into it.

par Pouya E

13 oct. 2019

Well-structured content, engaging programming assignments in honors track.

par David C

1 nov. 2016

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

par Camilo G

4 févr. 2020

Professor Koller does an amazing job, I fully recommend this course

par PRABAL B D

1 sept. 2018

Awesome Course. I got to learn a lot of useful concepts. Thank You.

par Pham T T

13 déc. 2019

Excellent course! This course helps me so much studying about PGM!

par Lik M C

12 janv. 2019

A great course! The provided training clarifies all key concepts

par SIVARAMAKRISHNAN V

6 janv. 2017

Great course. Thanks Daphne Koller, this is really motivating :)

par Arjun V

3 déc. 2016

A great course, a must for those in the machine learning domain.

par CIST N

30 oct. 2019

Good way to learn Probabilistic Graphical Models in practical

par Prazzy S

20 janv. 2018

Challenging! Regret not doing the coding assignment for honors

par Gautam B

4 juil. 2017

Great course loved the ongoing feedback when doing the quizes.

par Achen

6 mai 2018

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

par albert b

4 nov. 2017

Best course anywhere on this topic. Plus Daphne is the best !

par Arthur C

4 juin 2017

Super useful if you want to understand any probability model.

par Vu P

18 mars 2020

Great course, learned a lots. Thanks professor Daphne Koller

par Sriram P

24 juin 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.