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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

1,176 classificações
253 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


Jul 13, 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!!


Oct 23, 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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201 — 225 de {totalReviews} Avaliações para o Probabilistic Graphical Models 1: Representation

por Luiz C

Jun 26, 2018

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

por Werner N

Dec 28, 2016

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

por Haitham S

Nov 24, 2016

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

por Kevin W

Jan 17, 2017

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

por Péter D

Oct 29, 2017

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

por Andres P N

Jun 27, 2018

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

por Ahmad E

Aug 20, 2017

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

por Soteris S

Nov 27, 2017

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


Oct 04, 2016

Great and well paced content.

Quizzes really helps nailing the tricky points.

por Caio A M M

Dec 03, 2016

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

por Michael B

Dec 12, 2019

Honors seems like a must to full instill concepts/implementation

por Anshuman S

May 08, 2019

I would recommend adding some supplemental reading material.

por Jhonatan d S O

May 25, 2017

Rich content and useful tools for applying in real problems

por Alberto C

Dec 01, 2017

Theory: Very interesting. Assignments: not so useful.

por Yuanduo H

Jan 20, 2020

Five stars minus the week 4 coding homework

por Arthur B

Jan 08, 2017

More feedback from TA would be appreciated

por Myoungsu C

Dec 26, 2018

Writing on the ppt is not clear to see.

por Soumyadipta D

Jul 16, 2019

lectures are too fast otherwise great

por Sunsik K

Jul 31, 2018

Broad introduction to general issues

por Tianyi X

Feb 20, 2018

Lack of top-down review of the PGM.

por Sunil

Sep 12, 2017

Great intro to probabilistic models

por Nikesh B

Nov 06, 2016


por tyang16

Jun 20, 2019

too hard

por Yashwanth M

Jan 05, 2020


por Paul C

Oct 31, 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.