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返回到 Probabilistic Graphical Models 1: Representation

學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋

4.7
1,268 個評分
278 條評論

課程概述

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....

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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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126 - Probabilistic Graphical Models 1: Representation 的 150 個評論(共 271 個)

創建者 Roger T

Mar 05, 2017

very challenging class but very rewarding as well!

創建者 吕野

Dec 26, 2016

Good course lectures and programming assignments

創建者 Mahmoud S

Feb 25, 2019

Very good explanation and excellent assignments

創建者 Lilli B

Feb 02, 2018

Brilliant content and charismatic lecturer!!!

創建者 Fabio S

Sep 25, 2017

Excellent, well structured, clear and concise

創建者 llv23

Jul 19, 2017

Very good and excellent course and assignment

創建者 Parag H S

Aug 14, 2019

Learn the basic things in probability theory

創建者 Jonathan H

Nov 25, 2017

This course is hard and very interesting!

創建者 Shengliang X

May 29, 2017

excellent explanations! Thanks professor!

創建者 Alexander K

May 16, 2017

Thank you for all. This is gift for us.

創建者 Chahat C

May 04, 2019

lectures not good(i mean not detailed)

創建者 Harshdeep S

Jul 19, 2019

Excellent blend of maths & intuition.

創建者 NARENDRAN

Mar 07, 2020

Very good explanation on the subject

創建者 Jui-wen L

Jun 21, 2019

Easy to follow and very informative.

創建者 Miriam F

Aug 27, 2017

Very nice and well prepared course!

創建者 Gary H

Mar 28, 2018

Great instructor and information.

創建者 Subham S

Apr 29, 2020

I enjoyed the course very much!

創建者 George S

Jun 18, 2017

Excellent material presentation

創建者 郭玮

Apr 26, 2019

Really nice course, thank you!

創建者 hyesung J

Oct 10, 2019

So difficult. But interesting

創建者 Jinsun P

Jan 17, 2017

Really Helpful for Studying!

創建者 Shengding H

Mar 10, 2019

A very nice-designed course

創建者 Marno B

Feb 03, 2019

Absolutely love it!!!!

:)

創建者 Nguyễn L T Â

Feb 06, 2018

Thank you, the professor.

創建者 hy395

Sep 13, 2017

Very clear and intuitive.