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Volver a Probabilistic Graphical Models 2: Inference

Opiniones y comentarios de aprendices correspondientes a Probabilistic Graphical Models 2: Inference por parte de Universidad de Stanford

455 calificaciones
69 reseña

Acerca del 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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....

Principales reseñas

22 de ago. de 2019

Just like the first course of the specialization, 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.

19 de ago. de 2019

I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.

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26 - 50 de 71 revisiones para Probabilistic Graphical Models 2: Inference

por kat i

7 de dic. de 2020

Amazing course offering a technical as well as intuitional understanding of the principles of doing inference

por Evgeniy Z

10 de mar. de 2018

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.


19 de may. de 2020

Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts

por Una S

2 de sep. de 2020

Amazing course! Loved how Daphne explained very complicated things in an understandable manner!

por Sriram P

24 de jun. de 2017

Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am

por Jerry A R

22 de dic. de 2017

Great course! Expect to spend significant time reviewing the material.

por Anil K

5 de nov. de 2017

This course induces lateral thinking and deep reasoning.

por Liu Y

18 de mar. de 2018

Really a interesting, challenging and great course!

por KE Z

29 de dic. de 2017

Very valuable course! I am glad I made it.

por Tim R

4 de oct. de 2017

Very interesting, more advanced material

por Arthur C

19 de jul. de 2017

Difficult, but it makes you think a lot!

por chen h

5 de feb. de 2018

Interest but difficult.

por Simon T

14 de sep. de 2017

Great job Prof. Koller!

por Musalula S

2 de ago. de 2018

This is a great course

por Wei C

6 de mar. de 2018

good way to learn PGM,

por Alexander K

3 de jun. de 2017

Thank You for all.

por 王文君

21 de may. de 2017

Awesome class!

por 郭玮

12 de nov. de 2019

Very helpful.

por Anderson R L

3 de nov. de 2017

Great course!

por Alireza N

12 de ene. de 2017


por hanbt

8 de jun. de 2018

Very good

por Péter D

14 de nov. de 2017


por Ricardo A M C

18 de ene. de 2021


por Michael K

24 de dic. de 2016

The course lectures are even better than PGM I, as it appears that Professor Koller has recorded some material recently that helps fill in small holes from the previously recorded lectures. Hopefully she'll have time to clean up PGM I in the near future for future students.

This course is another tour-de-force for debugging, though it definitely made me a better programmer (I'm intermediate). I wish that the Discussion Boards were more active, and it's a shame that the Mentors were Missing In Action. On the one hand, the programming instructions were sometimes a bit vague, which made the assignments less like assignments are more like research projects. For these 2 reasons, the course is 4-star rather than 5-star.

Still, it's a lot better than trying to learn this out of the book by oneself. Some say enrollment has dropped off since they began charging for getting access to Quizzes and Programming Assignments. Or it may be attrition, as these are pretty challenging (and well taught) courses. I'm very happy to support this course financially, as it's loads cheaper than what I'd be paying if I were back at Stanford.

Like PGM I, I strongly recommend doing the Honors Programming Assignments, as it's really the way to learn the material well.

por mgbacher

19 de ene. de 2021

I believe the course is well dictated, well organized, and follows Koller's book and so it has to be read along with the lectures. Yet, I also think that improvements are a must. The following are points I would suggest taking into account: (1) it is very frustrating to have a final exam that can only be taken every 24 Hs. The course is indeed intense so having this limitation does not improve the level. (2) practical assignments are hard to understand, no feedback is given, and sometimes I have the feeling that the instructions were cut out from some other larger more understandable assignment, maybe at Standford. It is sometimes very frustrating to do them without a clear connection to lectures or a clear understanding of what is being asked to do. Overall, I strongly suggest doing the whole specialization with accompanying material, e.g., Koller's or Murphy's books. Alone the lectures are not enough and good that so it is.