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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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256 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

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!!

CM

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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76 — 100 de {totalReviews} Avaliações para o Probabilistic Graphical Models 1: Representation

por Johannes C

Mar 08, 2018

necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

por Alexandru I

Nov 25, 2018

Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

por Rajmadhan E

Aug 07, 2017

Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

por Lucian B

Jan 15, 2017

Some more exam questions and variation, including explanations when failing, would be very useful.

por BOnur b

Nov 13, 2018

Great course. Recommended to everyone who have interest on bayesian networks and markov models.

por Elvis S

Oct 29, 2016

Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

por Youwei Z

May 20, 2018

Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

por Umais Z

Aug 23, 2018

Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

por Hao G

Nov 01, 2016

Awesome course! I feel like bayesian method is also very useful for inference in daily life.

por Stephen F

Feb 26, 2017

This is a course for those interested in advancing probabilistic modeling and computation.

por liang c

Nov 15, 2016

Great course. and it is really a good chance to study it well under Koller's instruction.

por Ning L

Oct 18, 2016

This is a very good course for the foundation knowledge for AI related technologies.

por Abhishek K

Nov 06, 2016

Difficult yet very good to understand even after knowing about ML for a long time.

por chen h

Jan 21, 2018

The exercise is a little difficult. Need to revise several times to fully digest.

por Isaac A

Mar 23, 2017

A great introduction to Bayesian and Markov networks. Challenging but rewarding.

por 庭緯 任

Jan 10, 2017

perfect lesson!! Although the course is hard, the professor teaches very well!!

por Alejandro D P

Jun 30, 2018

This and its sequels, the most interesting Coursera courses I've taken so far.

por Naveen M N S

Dec 13, 2016

Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

por Amritesh T

Nov 25, 2016

highly recommended if you wanna learn the basics of ML before getting into it.

por Pouya E

Oct 13, 2019

Well-structured content, engaging programming assignments in honors track.

por David C

Nov 01, 2016

If you are interested in graphical models, you should take this course.

por Camilo G

Feb 05, 2020

Professor Koller does an amazing job, I fully recommend this course

por PRABAL B D

Sep 01, 2018

Awesome Course. I got to learn a lot of useful concepts. Thank You.

por Pham T T

Dec 13, 2019

Excellent course! This course helps me so much studying about PGM!

por Lik M C

Jan 12, 2019

A great course! The provided training clarifies all key concepts