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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

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1,378 classificações
307 avaliações

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

ST

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!!

CM

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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276 — 300 de 300 Avaliações para o Probabilistic Graphical Models 1: Representation

por Kervin P

5 de jan de 2017

This is an amazing course, and taught by an extremely talented and accomplished professor. I believe it's a must for anyone in AI/ML or Statistical Inference. The problem is that you're essentially on your own the entire course. There isn't any community or TA help to speak off. And the project is done in Matlab, so you end up wrestling with Matlab or Octave instead of actually doing and learning. I still recommend the course, but that's only because the material is so extremely important.

por Daniel S

11 de dez de 2019

Prof. Koller is exceptional. However, the focus of the course is toward the "theory" and less towards applications, unless one chooses to complete the Honors section of the course. I personally did not have the time to learn a new language syntax to attempt the Honors section...which is a shame. I do hope that this course is updated where R/Python replaces Octave/MatLab, because it would allow professional analysts more opportunity to explore the Honors content. Thanks!

por Владимир Д

11 de abr de 2020

Useful course on great subject, but poorly explained and supported. It was quite hard for me to get implicit ideas and Honors assignments. I ended up skipping Honors assignments since they're explained really really poorly and most of the time I spent trying to figure out what I'm required to do. Forums are inactive and no mentors reply to the posts. I don't recommend taking this course if you don't have someone to guide and help you.

por Sharon M

1 de abr de 2021

The course content is really interesting and Daphne Koller is a fabulous presenter. Unfortunately, though, you are doing this course on your own - looks like there have been no TAs online for over 3 years, and if you're looking for support or assistance understanding any of the work you may find confusing or difficult then don't expect to get it here. Very disappointed that a paid course has virtually no support in it whatsoever.

por Sami J

22 de abr de 2020

Material is interesting but needs updating. Programming assignments have been marked as "Honors Assignments", which is a thinly veiled attempt to shirk responsibility for fixing bugs and providing student support. Quiz questions are vaguely worded. Overall the course is challenging, but only sometimes for the right reasons.

por Shaun M

7 de set de 2021

Information is well presented. Tests are 4 questions. Any mistake in the answer counts as wrong, and all questions must be correct to receive the passing 80%. The course makes you wait an hour to retake the exam, so it is NOT friendly for folks on a time schedule.

por Shen C

14 de jul de 2020

this course is a very difficult one. takes a lot of time and effort. forum is really useful (i wouldn't have passed without it). that said, it is also because there is little help from the lecturer and instructors. would appreciate more help.

por Vladimir R

12 de jan de 2021

Great topic, the professor is a top expert in the field, but the grading interface badly needs an upgrade. It is not acceptable for students to have to manually hack JSON submissions just to get around grader errors.

por Christos G

9 de mar de 2018

Quite difficult, not much help in discussion forums, some assignmnents had insufficient supporting material and explanations, challenging overall, I thought at least 3-4 times to abandon it.

por Siavash R

10 de ago de 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

por roma g

4 de nov de 2016

The audio is VERY VERY poor.

That makes it very hard to understand what Prof Kohler is trying to impart on us..

I often lost track

por Xingjian Z

2 de nov de 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

por Ujjval P

13 de dez de 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

por Jonathan K

26 de jan de 2018

Interesting and useful material, but I found the lecturer unengaging.

por Michel S

14 de jul de 2018

Good course, but the material really needs a refresh!

por Robert M

6 de fev de 2018

Started off well. Finished poorly

por Peter

29 de set de 2016

The content seems to be excellent regarding "what" is presented. But sadly the sound quality is rather bad: Sounds like an age-old valve radio with A LOT of dropouts. And Professor Daphne is an agile and therefore less disciplined speaker which lessens the understandability of her speech in conjunction with the poor sound quality furthermore. Especially for me as a non-native foreign english speaker it is very hard to follow. And now I am at one point in the course, that is "Flow of Probalistic Influence", where she explains a concept without explaining what is meant with the used underlying notions "flow" and "influence" which makes me difficult to understand what is going on. That means in my point of view that the slides are not sufficiently prepared. Although I'm very interested in the topic I am asking myself after the first view videos if I should continue or drop because my cognitive capacitity is for me to worthful to use it for the decoding of badly prepared and presented material. Ok, my decision heuristic in such cases is "Use the hammer not the tweezers!". Therefore I have dropped. Please improve the state of this class from beta to release. Then I will come back.

por Jennifer H

15 de dez de 2019

Quite abstract. A solid mathematical grounding, but largely devoid of practicalities. Optional exercises are quite basic, and don't get to the heart of the matter. Lectures are confusing, as undefined terminology come up out of the blue, and key concepts aren't clearly explained.

por Oleg P

9 de dez de 2021

The instructor doesn't teach but just very quickly reads the material on the language of those people who already knows the material like she tries to pass some exam. Very hard to learn anything.

por Roman F

11 de mar de 2021

This course is poorly structured, the material is poorly explained, the lecturer is going too fast and does not stress important concepts, video, and sound quality are below average. Do not recommend.

The structure of this course is an example of how not to teach mathematics. Examples before definitions and introduction of general concepts, lack of direction and "big picture" context, unexcusable things like "let's prove it by example"... It is very frustrating and almost impossible to follow.

por Aswin T

10 de set de 2020

Very rigid questions, very theoretical. Very poor instructor support. Content needs to be improved. Very disconnected approach.

por Ahmad C

11 de jun de 2020

very shallow explanation of important concepts

por Shan-Jyun W

24 de jun de 2017

Lectures are awful.

por Belal M

8 de set de 2017

A very dry course.

por Javier G

4 de ago de 2020

Muy malo