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Learner Reviews & Feedback for Probabilistic Graphical Models 3: Learning by Stanford University

4.6
stars
299 ratings

About the Course

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 third in a sequence of three. Following the first course, which focused on representation, and the second, which focused on inference, this course addresses the question of learning: how a PGM can be learned from a data set of examples. The course discusses the key problems of parameter estimation in both directed and undirected models, as well as the structure learning task for directed models. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of two commonly used learning algorithms are implemented and applied to a real-world problem....

Top reviews

SP

Oct 11, 2020

An amazing course! The assignments and quizzes can be insanely difficult espceially towards the conclusion.. Requires textbook reading and relistening to lectures to gather the nuances.

OD

Jan 29, 2018

very good course for PGM learning and concept for machine learning programming. Just some description for quiz of final exam is somehow unclear, which lead to a little bit confusing.

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51 - 53 of 53 Reviews for Probabilistic Graphical Models 3: Learning

By Siwei Y

•

Feb 3, 2017

上课的方式过于抽象艰涩, 即便是谈到实际应用例子也是说得云里雾里的. 而且练习跟课里的内容联系不紧密. 这样导致为了通过练习和最后考试, 很多时候 是利用考试策略或者说穷举排除法. 也就是说其实学生没有真正理解课里的概念. 还是那句话,我相信有人能上得比这个好的多. 有人说上此课需要有一定的背景知识,我想说, 那些有一定背景知识的人也不需要上这个课了. 最后真心感谢牛姐介绍了如此多有意思的东西. 感谢她们团队设计的PA . 这个东西确实不容易.

By Aph.d. L

•

Nov 18, 2023

Cannot submit any assignments because of the bad code of submit.m And cannot get any help from course forum.

By Jiaxing L

•

Feb 11, 2017

Managed to be get worse and worse