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Probabilistic Graphical Models 3: Learning, Stanford University

174 个评分
28 个审阅


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....


创建者 ZZ

Feb 14, 2017

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.

创建者 JR

Jan 29, 2018

Great course! It is pretty difficult - be prepared to study. Leave plenty of time before the final exam.


28 个审阅

创建者 Antônio Horta Ribeiro

Nov 06, 2018

Bad choice of content. Focus too much on the specific case of table CPDs, missing the big picture.

创建者 Luis

Aug 28, 2018

Great course, though with the progress of ML/DL, content seems a touch outdated. Would

创建者 Liu Yang

Aug 27, 2018

Great course, great assignments I indeed learn much from this course an the whole PGM ialization!

创建者 Musalula Sinkala

Aug 25, 2018

The course is very involved but Daphne makes its palatable. The course open a new world of new possibilities where one can apply PGMs to get concrete understanding of relationships between events and phenomena in any discipline; from social sciences to natural sciences.

创建者 Michel Speiser

Jul 14, 2018

Good course, but the material really needs a refresh!

创建者 Gorazd Hribar Rajterič

Jul 07, 2018

A very demanding course with some glitches in lectures and materials. The topic itself is very interesting, educational and useful.

创建者 Vincent Li

Jun 05, 2018

Difficult; requires textbook reading to complete. I could not get samiam to work so I skipped the initial PA. The PA are challenging as well but well worth it if you want to understand how to implement PGMs.

创建者 Rishi Chopra

Jun 05, 2018

The course facilitates learning - and reinforces acquired knowledge through the simple principle of honest effort: students are not given all the answers... but they are 'nudged' in the right direction & guided towards fruitful questions; in a way, it's the perfect course!

创建者 Chan-Se-Yeun

Feb 22, 2018

Yeah! I managed to finish PGM. I feel ready to explore further. PGM 3 is really helpful. Although many details are not fully discussed, some important intuitions are well illustrated, like EM algorithm and its modification in case of incomplete data. Also, the way the teacher teach set an good example for me to learn to demonstrate complicated things in an easy and vivid way. Thank you so much!

创建者 llv23

Jan 30, 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.