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Voltar para Probabilistic Graphical Models 2: Inference

Comentários e feedback de alunos de Probabilistic Graphical Models 2: Inference da instituição Universidade de Stanford

470 classificações
73 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 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


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 74 Avaliações para o Probabilistic Graphical Models 2: Inference

por Julio C A D L

9 de abr de 2018

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

por kat i

7 de dez 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 mai de 2020

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

por Una S

2 de set de 2020

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

por Martin P

20 de jan de 2021

Great course! Course has filled gaps in my knowledge from statistics and similar sciences.

por Ruiliang L

24 de fev de 2021

Awesome class to gain better understanding of inference for graphical model

por Sriram P

24 de jun de 2017

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

por Jerry R

22 de dez 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 dez de 2017

Very valuable course! I am glad I made it.

por Tim R

4 de out de 2017

Very interesting, more advanced material

por Arthur C

19 de jul de 2017

Difficult, but it makes you think a lot!

por Dat Q D

26 de jan de 2022

the content is very hard

por chen h

5 de fev de 2018

Interest but difficult.

por Ram G

14 de set 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 Wenjun W

21 de mai 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 jan de 2017


por hanbt

8 de jun de 2018

Very good