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返回到 Probabilistic Graphical Models 2: Inference

学生对 斯坦福大学 提供的 Probabilistic Graphical Models 2: Inference 的评价和反馈

470 个评分
73 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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem....



Aug 22, 2019

Just like the first course of the specialization, this course is really good. It is well organized and taught in the best way which really helped me to implement similar ideas for my projects.


Aug 19, 2019

I have clearly learnt a lot during this course. Even though some things should be updated and maybe completed, I would definitely recommend it to anyone whose interest lies in PGMs.


26 - Probabilistic Graphical Models 2: Inference 的 50 个评论(共 74 个)

创建者 Julio C A D L

Apr 9, 2018

I would have like to complete the honors assignments, unfortunately, I'm not fluent in Matlab. Otherwise, great course!

创建者 kat i

Dec 7, 2020

Amazing course offering a technical as well as intuitional understanding of the principles of doing inference

创建者 Evgeniy Z

Mar 10, 2018

Very interesting course. However, even after completing it with honors, I feel like I don't understand a lot.


May 19, 2020

Great balance between theories and practices. Also provide a lot of intuitions to understand the concepts

创建者 Una S

Sep 2, 2020

Amazing course! Loved how Daphne explained very complicated things in an understandable manner!

创建者 Martin P

Jan 20, 2021

Great course! Course has filled gaps in my knowledge from statistics and similar sciences.

创建者 Ruiliang L

Feb 24, 2021

Awesome class to gain better understanding of inference for graphical model

创建者 Sriram P

Jun 24, 2017

Had a wonderful and enriching fun filled experience, Thank you Daphne Ma'am

创建者 Jerry R

Dec 22, 2017

Great course! Expect to spend significant time reviewing the material.

创建者 Anil K

Nov 5, 2017

This course induces lateral thinking and deep reasoning.

创建者 Liu Y

Mar 18, 2018

Really a interesting, challenging and great course!

创建者 KE Z

Dec 29, 2017

Very valuable course! I am glad I made it.

创建者 Tim R

Oct 4, 2017

Very interesting, more advanced material

创建者 Arthur C

Jul 19, 2017

Difficult, but it makes you think a lot!

创建者 Dat Q D

Jan 26, 2022

the content is very hard

创建者 chen h

Feb 5, 2018

Interest but difficult.

创建者 Ram G

Sep 14, 2017

Great job Prof. Koller!

创建者 Musalula S

Aug 2, 2018

This is a great course

创建者 Wei C

Mar 6, 2018

good way to learn PGM,

创建者 Alexander K

Jun 3, 2017

Thank You for all.

创建者 Wenjun W

May 21, 2017

Awesome class!

创建者 郭玮

Nov 12, 2019

Very helpful.

创建者 Anderson R L

Nov 3, 2017

Great course!

创建者 Alireza N

Jan 12, 2017


创建者 hanbt

Jun 8, 2018

Very good