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

1,320 évaluations
294 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

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!!

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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226 - 250 sur 287 Avis pour Probabilistic Graphical Models 1: Representation

par Roman S

20 mars 2018

A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.

par serge s

18 oct. 2016

Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

par Jack A

5 nov. 2017

The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

par Francois L

16 mars 2020

Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)

par Gorazd H R

7 juil. 2018

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

par Ashwin P

9 janv. 2017

Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

par Forest R

20 févr. 2018

Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

par Иван М

26 avr. 2020

Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.

par Victor Z

22 déc. 2018

Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

par Luiz C

26 juin 2018

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

par Saurabh N

24 mars 2020

The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

par Werner N

28 déc. 2016

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

par Haitham S

24 nov. 2016

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

par Kevin W

17 janv. 2017

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

par Péter D

29 oct. 2017

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

par Andres P N

27 juin 2018

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

par Ahmad E

20 août 2017

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

par Soteris S

27 nov. 2017

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


4 oct. 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

par Caio A M M

2 déc. 2016

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

par Michael B

12 déc. 2019

Honors seems like a must to full instill concepts/implementation

par Anshuman S

7 mai 2019

I would recommend adding some supplemental reading material.

par Jhonatan d S O

25 mai 2017

Rich content and useful tools for applying in real problems

par Vahan A

31 mai 2020

Please, provide programming assignments on Python or C++

par Alberto C

1 déc. 2017

Theory: Very interesting. Assignments: not so useful.