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Probabilistic Graphical Models 2: Inference に戻る

スタンフォード大学(Stanford University) による Probabilistic Graphical Models 2: Inference の受講者のレビューおよびフィードバック

4.6
469件の評価
73件のレビュー

コースについて

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....

人気のレビュー

AT

2019年8月22日

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.

AL

2019年8月19日

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.

フィルター:

Probabilistic Graphical Models 2: Inference: 26 - 50 / 74 レビュー

by Julio C A D L

2018年4月9日

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

by kat i

2020年12月7日

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

by Evgeniy Z

2018年3月10日

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

by HARDIAN L

2020年5月19日

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

by Una S

2020年9月2日

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

by Martin P

2021年1月20日

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

by Ruiliang L

2021年2月24日

Awesome class to gain better understanding of inference for graphical model

by Sriram P

2017年6月24日

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

by Jerry R

2017年12月22日

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

by Anil K

2017年11月5日

This course induces lateral thinking and deep reasoning.

by Liu Y

2018年3月18日

Really a interesting, challenging and great course!

by KE Z

2017年12月29日

Very valuable course! I am glad I made it.

by Tim R

2017年10月4日

Very interesting, more advanced material

by Arthur C

2017年7月19日

Difficult, but it makes you think a lot!

by Dat Q D

2022年1月26日

the content is very hard

by chen h

2018年2月5日

Interest but difficult.

by Ram G

2017年9月14日

Great job Prof. Koller!

by Musalula S

2018年8月2日

This is a great course

by Wei C

2018年3月6日

good way to learn PGM,

by Alexander K

2017年6月3日

Thank You for all.

by Wenjun W

2017年5月21日

Awesome class!

by 郭玮

2019年11月12日

Very helpful.

by Anderson R L

2017年11月3日

Great course!

by Alireza N

2017年1月12日

Excellent!

by hanbt

2018年6月8日

Very good