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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

4.6
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.

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

创建者 Volodymyr D

Apr 11, 2020

Useful course on great subject, but poorly explained and supported. It was quite hard for me to get implicit ideas and Honors assignments. I ended up skipping Honors assignments since they're explained really really poorly and most of the time I spent trying to figure out what I'm required to do. Forums are inactive and no mentors reply to the posts. I don't recommend taking this course if you don't have someone to guide and help you.

创建者 Sami J

Apr 22, 2020

Material is interesting but needs updating. Programming assignments have been marked as "Honors Assignments", which is a thinly veiled attempt to shirk responsibility for fixing bugs and providing student support. Quiz questions are vaguely worded. Overall the course is challenging, but only sometimes for the right reasons.

创建者 Shen C

Jul 14, 2020

this course is a very difficult one. takes a lot of time and effort. forum is really useful (i wouldn't have passed without it). that said, it is also because there is little help from the lecturer and instructors. would appreciate more help.

创建者 Siavash R

Aug 10, 2017

For me this was a difficult course not because of the material, but because of the teaching style. I don't think Dr. Koller is a very good teacher.

创建者 Xingjian Z

Nov 2, 2017

Fun topic. But the explanation of the mentor is somewhat vague and the material is sometimes outdated and misleading.

创建者 Ujjval P

Dec 13, 2016

Concepts covered in quiz and assignments are not covered well in the lecture videos, can be much better.

创建者 Jonathan K

Jan 25, 2018

Interesting and useful material, but I found the lecturer unengaging.

创建者 Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

创建者 Robert M

Feb 6, 2018

Started off well. Finished poorly

创建者 Aswin T

Sep 10, 2020

Very rigid questions, very theoretical. Very poor instructor support. Content needs to be improved. Very disconnected approach.

创建者 Deleted A

Jun 11, 2020

very shallow explanation of important concepts

创建者 Shan-Jyun W

Jun 24, 2017

Lectures are awful.

创建者 Belal M

Sep 8, 2017

A very dry course.

创建者 Javier G

Aug 4, 2020

Muy malo