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学生对 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的评价和反馈

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302 条评论


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....


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!!

Oct 22, 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).


101 - Probabilistic Graphical Models 1: Representation 的 125 个评论(共 295 个)

创建者 Una S

Jul 24, 2020

Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

创建者 liang c

Nov 15, 2016

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

创建者 AlexanderV

Mar 9, 2020

Great course, except that the programming assignments are in Matlab rather than Python

创建者 Ning L

Oct 17, 2016

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

创建者 Hong F

Jun 21, 2020

Hope there are explanations of the hard questions (marked by *) in the final exam.

创建者 Abhishek K

Nov 6, 2016

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

创建者 chen h

Jan 20, 2018

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

创建者 Isaac A

Mar 23, 2017

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

创建者 庭緯 任

Jan 10, 2017

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

创建者 Alejandro D P

Jun 29, 2018

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

创建者 Naveen M N S

Dec 13, 2016

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

创建者 Amritesh T

Nov 25, 2016

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

创建者 Pouya E

Oct 13, 2019

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

创建者 David C

Nov 1, 2016

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

创建者 Camilo G

Feb 4, 2020

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


Sep 1, 2018

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

创建者 Pham T T

Dec 13, 2019

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

创建者 Lik M C

Jan 12, 2019

A great course! The provided training clarifies all key concepts

创建者 Sivaramakrishnan V

Jan 6, 2017

Great course. Thanks Daphne Koller, this is really motivating :)

创建者 Arjun V

Dec 3, 2016

A great course, a must for those in the machine learning domain.

创建者 CIST N

Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

创建者 Prazzy S

Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

创建者 Gautam B

Jul 4, 2017

Great course loved the ongoing feedback when doing the quizes.

创建者 Dino

May 6, 2018

a bit too hard if you don't have enough probability knowledge

创建者 albert b

Nov 4, 2017

Best course anywhere on this topic. Plus Daphne is the best !