Chevron Left
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
476件の評価

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

by kat i

2020年12月7日

by Evgeniy Z

2018年3月10日

by HARDIAN L

2020年5月19日

by Una S

2020年9月2日

by Martin P

2021年1月20日

by Ruiliang L

2021年2月24日

by Sriram P

2017年6月24日

by Jerry R

2017年12月22日

by Anil K

2017年11月5日

by Liu Y

2018年3月18日

by KE Z

2017年12月29日

by Tim R

2017年10月4日

by Arthur C

2017年7月19日

by Dat Q D

2022年1月26日

by chen h

2018年2月5日

by Ram G

2017年9月14日

by Musalula S

2018年8月2日

by Wei C

2018年3月6日

by Alexander K

2017年6月3日

by Wenjun W

2017年5月21日

by 郭玮

2019年11月12日

by Anderson R L

2017年11月3日

by Alireza N

2017年1月12日

by hanbt

2018年6月8日