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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

4.6
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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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251 — 275 de 300 Avaliações para o Probabilistic Graphical Models 1: Representation

por Andres P N

27 de jun de 2018

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

por Ahmad E

20 de ago de 2017

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

por Soteris S

27 de nov de 2017

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

por mathieu.zaradzki@gmail.com

4 de out de 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

por Caio A M M

2 de dez de 2016

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

por Michael B

12 de dez de 2019

Honors seems like a must to full instill concepts/implementation

por Anshuman S

7 de mai de 2019

I would recommend adding some supplemental reading material.

por Jhonatan d S O

25 de mai de 2017

Rich content and useful tools for applying in real problems

por Vahan A

31 de mai de 2020

Please, provide programming assignments on Python or C++

por Alberto C

1 de dez de 2017

Theory: Very interesting. Assignments: not so useful.

por Yuanduo H

19 de jan de 2020

Five stars minus the week 4 coding homework

por Arthur

8 de jan de 2017

More feedback from TA would be appreciated

por Ian M C

26 de dez de 2018

Writing on the ppt is not clear to see.

por Soumyadipta D

16 de jul de 2019

lectures are too fast otherwise great

por sunsik k

31 de jul de 2018

Broad introduction to general issues

por Tianyi X

20 de fev de 2018

Lack of top-down review of the PGM.

por Sunil

12 de set de 2017

Great intro to probabilistic models

por Nikesh B

6 de nov de 2016

Excellent

por Tianqi Y

19 de jun de 2019

too hard

por Yashwanth M

5 de jan de 2020

Good

por Ricardo A M C

9 de jan de 2021

ok

por Paul C

31 de out de 2016

I found plenty of useful information in this course overall but lectures often spent too much time dwelling on the detail of simpler concepts while more complex areas, and sometimes critical information that was later built upon, were only touched briefly or sometimes skipped entirely. I missed a sense of continuity as we skipped from model to model with a minimum of time spent on how the models complement each other and their relative strengths and weaknesses in application.

The way data structures were defined in the code was particularly difficult to deal with. The coding exercises all suffered as a result. It ended up taking way too much time to figure how to decode the data and trace logic around it. This meant that grasping concepts and learning from the questions came in a distant second priority to debugging.

Dr Koller mentioned that the material is aimed at postgraduates. I felt that the level of content covered here would just as easily be grasped by most undergraduates in technical disciplines if it had been delivered in a more structured manner with clearer progression across models (conceptually and mathematically) and better code examples. When delivering in this format, allowances need to be made for the facts that tutorial sessions do not exist and the possibilities for informal Q&A are limited so any gaps become very difficult for students to fill in themselves.

Despite the above shortcomings I'm glad I did the course and I would still recommend it to someone interested in graphical models as it does cover the basics well enough to make a decent start. I'm not sure whether or not I'd recommend the programming exercises as they are a significant time sink but at the same time, without spending time attacking the programming problems the concepts are not likely to gel based on the video and quizzes alone.

por Nicholas E

29 de out de 2016

The course was very interesting and thought-provoking. I found the introduction to probabilistic graphical models (PGMs) and their properties struck a nice balance between intuition and formalism. The discussions highlighted exciting aspects of their power in simplifying complex problems involving uncertainty. However, I still do not feel I could propose convincing PGMs for real-world problems. There are examples in the course, but they are far removed from being concrete applications. I would have preferred there be an in depth analysis of an application of PGMs in the literature over the lengthy programming assignments. I am an experienced programmer with over 5 years of experience in many languages including MATLAB/Octave and I sometimes found it uninspiring to solve toy problems, not due to the difficulty in using the programming language, but rather because after the assignment had been completed I felt I had not really learnt much more than I would have from just watching the lectures, although, if you are interested in getting experience with MATLAB/Octave, the programming assignments are good practice. I qualify this in stating that I have not yet completed the next two courses on PGMs; this course may present an essential foundation that is necessary for the upcoming courses, and in any case provoked my interest in learning more about them

por Mahendra K

4 de out de 2017

The course is highly theoretical. Would have been great if it was paced well and driven from real world examples. I am not saying that there are no examples. But it'd have been better if the concepts were driven via some real world examples instead of first talking about the concept and then its applications.

What would have been even better if Python was an option for PAs. Octave can't be used in industry setting where the amount of data is really large. Both Python and Octave should have been an option so that the student can decide for themself.

por John E M

31 de mar de 2018

Lectures were OK and quizzes and exams appropriately difficult. But Labs were pretty difficult especially lab 4 which I ended up surrendering on. This means I didn't do the accompanying quiz and gave up on the possibility of honors recognition as well.

While labs don't have to be as hand-holding as the DeepLearning class by Coursera, it would be nice to get more help and maybe not submit errors for the parts I haven't tackled yet when submitting (as DeepLearning and MachineLearning courses figured out how to do).