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Voltar para Probabilistic Graphical Models 2: Inference

Probabilistic Graphical Models 2: Inference, Stanford University

4.6
292 classificações
48 avaliações

Informações 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 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....

Melhores avaliações

por YP

May 29, 2017

I learned pretty much from this course. It answered my quandaries from the representation course, and as well deepened my understanding of PGM.

por JL

Apr 09, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

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49 avaliações

por Shi Yihui

Dec 16, 2018

It's absolutely very very hard but extremely interesting course! Although code assignments always have a lot of small bugs, and it cost me lots of time to find out, but, hey! Everything is the same in school(offline), nothing gonna be perfect. The sampling part is the most difficult stuff to learn so far, and after I tried to review it again and again, combined with other online material, I got those shit done! The only drawback of this course is that not many people active in the forum(Including those TA), maybe that just because only a small number of people enrolled in this course. In short, worth learning!

por Kaixuan Zhang

Dec 05, 2018

hope to get some feedbacks about hw or exam

por Larry Lyu

Nov 18, 2018

This course seems to have been abandoned by Coursera. Mentors never reply to discussion forum posts (if there is any active mentor at all). Many assignments and tests are confusing and misleading. There are numerous materials you can find online to learn about Graphical Models than spending time & money on this.

por Kalyan Dharanipragada

Nov 05, 2018

Great introduction.

It would be great to have more examples included in the lectures and slides.

por Musalula Sinkala

Aug 02, 2018

This is a great course

por Luis

Aug 01, 2018

Very good course. Subject is quiet complex: lack of concrete examples to make sure concepts well understood. Had to review each the Course twice to understand concepts well

por Michel Speiser

Jul 14, 2018

Good course, but the material really needs a refresh!

por Gorazd Hribar Rajterič

Jul 07, 2018

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

por Tomer Nahshon

Jun 20, 2018

The Programming assignment must be updated and become relevant... They are way too hard and not friendly...

por hanbt

Jun 08, 2018

Very good