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返回到 Probabilistic Graphical Models 1: Representation

学生对 Stanford University 提供的 Probabilistic Graphical Models 1: Representation 的评价和反馈

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1,441 个评分

课程概述

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....

热门审阅

RG

Jul 12, 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!!

AB

Aug 30, 2018

Excellent course, the effort of the instructor is well reflected in the content and the exercices. A must for every serious student on (decision theory or markov random fields tasks.

筛选依据：

151 - Probabilistic Graphical Models 1: Representation 的 175 个评论（共 314 个）

创建者 Ruiliang L

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Feb 15, 2021

Awesome class to gain solid understanding of graphical model

创建者 Phong V

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Mar 18, 2020

Great course, learned a lots. Thanks professor Daphne Koller

创建者 Sriram P

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Jun 24, 2017

Had a wonderful learning experience, Thank You Daphne Ma'am.

创建者 Pablo G M D

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Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

创建者 Henry H

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Nov 14, 2016

Very informative course, and incredibly useful in research

创建者 Ingyo J

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Oct 3, 2018

What a wonderful course that I haven't ever taken before.

创建者 Albedo

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Oct 29, 2022

Very good course. Thanks for ability to learn this.

创建者 EPerishko

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Jul 24, 2023

Nice and intensive lectures, very well structured.

创建者 Renjith K A

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Sep 23, 2018

Was really helpful in understanding graphic models

创建者 Roger T

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Mar 5, 2017

very challenging class but very rewarding as well!

创建者 Harshit A

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Apr 20, 2021

This is a challenging but very satisfying course.

创建者 吕野

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Dec 26, 2016

Good course lectures and programming assignments

创建者 Mahmoud S

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Feb 24, 2019

Very good explanation and excellent assignments

创建者 Lilli B

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Feb 2, 2018

Brilliant content and charismatic lecturer!!!

创建者 Fabio S

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Sep 25, 2017

Excellent, well structured, clear and concise

创建者 Orlando D

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Jul 19, 2017

Very good and excellent course and assignment

创建者 Parag S

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Aug 13, 2019

Learn the basic things in probability theory

创建者 Christian S

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Dec 11, 2020

Highest level in coursera courses so far.

创建者 Jonathan H

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Nov 25, 2017

This course is hard and very interesting!

创建者 Shengliang X

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May 29, 2017

excellent explanations! Thanks professor!

创建者 Alexander K

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May 15, 2017

Thank you for all. This is gift for us.

创建者 Chahat C

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May 4, 2019

lectures not good(i mean not detailed)

创建者 Harshdeep S

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Jul 19, 2019

Excellent blend of maths & intuition.

创建者 NARENDRAN

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Mar 6, 2020

Very good explanation on the subject

创建者 Jui-wen L

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Jun 20, 2019

Easy to follow and very informative.