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Voltar para Probabilistic Graphical Models 1: Representation

Comentários e feedback de alunos de Probabilistic Graphical Models 1: Representation da instituição Universidade de Stanford

1,402 classificações

Sobre o curso

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....

Melhores avaliações


12 de jul de 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 de out de 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 de 304 Avaliações para o Probabilistic Graphical Models 1: Representation

por John P

16 de jun de 2022

A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.

por Vivek G

27 de abr de 2019

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

por Sureerat R

2 de mar de 2018

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

por Angel G G

12 de dez de 2019

Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.

por Ayush T

23 de ago de 2019

This course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.

por Valeriy Z

13 de nov de 2017

This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.

por Mulang' O

31 de mar de 2019

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

por Singhi K

1 de ago de 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.

por Karam D

3 de abr de 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


16 de out de 2018

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

por Mike P

30 de jul de 2019

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

por Pathirage D

29 de mai de 2021

one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.

por Matt M

22 de out de 2016

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

por Samuel B

13 de mar de 2021

Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.

por Anton K

7 de mai de 2018

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

por Kelvin L

11 de ago de 2017

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

por 杨涛

27 de mar de 2019

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


23 de jun de 2018

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

por Satish P

12 de jul de 2020

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

por Johannes C

19 de abr de 2020

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

por Alexandru I

25 de nov de 2018

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

por Rajmadhan E

7 de ago de 2017

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

por Lucian

15 de jan de 2017

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

por Onur B

13 de nov de 2018

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

por Elvis S

28 de out de 2016

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