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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

1,176 個評分
253 條評論


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 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!!


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).


101 - Probabilistic Graphical Models 1: Representation 的 125 個評論(共 246 個)

創建者 CIST N

Oct 30, 2019

Good way to learn Probabilistic Graphical Models in practical

創建者 Prazzy S

Jan 20, 2018

Challenging! Regret not doing the coding assignment for honors

創建者 Gautam B

Jul 04, 2017

Great course loved the ongoing feedback when doing the quizes.

創建者 Achen

May 06, 2018

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

創建者 albert b

Nov 04, 2017

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

創建者 Arthur C

Jun 04, 2017

Super useful if you want to understand any probability model.

創建者 Sriram P

Jun 24, 2017

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

創建者 Pablo G M D

Jul 18, 2018

Outstanding teaching and the assignments are quite useful!

創建者 Ziheng

Nov 14, 2016

Very informative course, and incredibly useful in research

創建者 Ingyo C

Oct 04, 2018

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

創建者 Renjith K A

Sep 23, 2018

Was really helpful in understanding graphic models

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

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 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!