Chevron Left
Retour à Probabilistic Graphical Models 1: Representation

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

4.7
étoiles
1,297 évaluations
287 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).

Filtrer par :

76 - 100 sur 281 Avis pour Probabilistic Graphical Models 1: Representation

par Isaiah O M

31 mars 2019

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

par Saikat M

1 août 2017

Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.

par Mohammd K D

3 avr. 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 ALBERTO O A

16 oct. 2018

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

par Mike P

30 juil. 2019

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

par Matt M

22 oct. 2016

Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.

par Anton K

7 mai 2018

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

par Kelvin L

11 août 2017

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

par 杨涛

27 mars 2019

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

par HARDIAN L

23 juin 2018

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

par satish p

12 juil. 2020

A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.

par Johannes C

19 avr. 2020

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

par Alexandru I

25 nov. 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

par Rajmadhan E

7 août 2017

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

par Lucian B

15 janv. 2017

Some more exam questions and variation, including explanations when failing, would be very useful.

par Onur B

13 nov. 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

par Elvis S

28 oct. 2016

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

par Youwei Z

19 mai 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

par Umais Z

23 août 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

par Hao G

1 nov. 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

par Alfred D

2 juil. 2020

Was a little difficult in the middle but the last section summary just refreshed all of it

par Stephen F

26 févr. 2017

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

par Una S

24 juil. 2020

Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

par liang c

15 nov. 2016

Great course. and it is really a good chance to study it well under Koller's instruction.

par AlexanderV

9 mars 2020

Great course, except that the programming assignments are in Matlab rather than Python