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Probabilistic Graphical Models 1: Representation に戻る

スタンフォード大学(Stanford University) による Probabilistic Graphical Models 1: Representation の受講者のレビューおよびフィードバック



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 first in a sequence of three. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course discusses both the theoretical properties of these representations as well as their use in practice. The (highly recommended) honors track contains several hands-on assignments on how to represent some real-world problems. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly....



Prof. Koller did a great job communicating difficult material in an accessible manner. Thanks to her for starting Coursera and offering this advanced course so that we can all learn...Kudos!!


The course was deep, and well-taught. This is not a spoon-feeding course like some others. The only downside were some "mechanical" problems (e.g. code submission didn't work for me).


Probabilistic Graphical Models 1: Representation: 226 - 250 / 295 レビュー

by Boxiao M


The lecture was a bit too compact and unsystematic. However, if you also do a lot of reading of the textbook, you can learn a lot. Besides, the Quiz and Programming task are of high qualities.

by Shawn C


The course is great with plenty of knowledge. A little defect is about description about assignment. As the forum discussed, several quizzes may confusing.

by Shane C


concepts in the videos are well presented. additional readings from the textbook are helpful to cement concepts not explained as thoroughly in the videos

by Hilmi E


I really enjoyed attending this course. It is foundational material for anyone who wants to use graphical models for inference and decision making..

by Nimo F B


Great content and easy to pick up. Only issue was with downloaded Octave software. Does not work, despite multiple downloads on different machines

by Roman S


A good introduction to PGM, from very basic concepts to some move in-depth features. A big disadvantage is Matlab/Octave programming assignments.

by Serge S


Thanks to this course, Probabilistic Graphical Models are not anymore an esoteric subject! I am really looking for the second part of the course.

by Jack A


The class was very exciting and challenging, but I felt the programming assignments weren't dependent on understanding the classwork at all.

by Francois L


Really interesting contents but it would be great to have the exercises in a more up to date programming environment (python for instance)

by Gorazd H R


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

by Ashwin P


Great material. Course mentors are nowhere to be found and some of the problems are hard, so I'd have liked to see some guidance.

by Forest R


Excellent introduction into probabilistic graph models. Introduced me to Baysian analysis and is quite helpful for my work.

by Иван М


Great course, would be nicer if exercises were in Python or R and if software from first honours task worked on Mac.

by Victor Z


Some interesting knowledges about PCM, but I think I need more detailed information in the succeeding courses.

by Luiz C


Good course, quite complex, wish some better quality slides, and more quizzes to help understand the theory

by Saurabh N


The coding assignments can be compulsory too.

Maybe not as vast, but maybe interleaved with the quizzes

by Werner N


Very good course. It should contain more practical examples to make the material better to understand.

by Haitham S


Great course, however, the honors track assignments are a bit too tedious and take lots of time.

by Kevin W


The course is pretty good. I love the way that the professor led us into the graphical models.

by Péter D


great job, although the last PA is a huge pain / difficulty spike - more hints would be nice

by Andres P N


There are many error in the implementations for octave. Aside from that, the course is fine

by Ahmad E


Covers some material a little too quickly, but overall a good and entertaining course.

by Soteris S


A bit more challenging than I thought but very useful, and very well structured



Great and well paced content.

Quizzes really helps nailing the tricky points.

by Caio A M M


Instructor is engaging in her delivery. Topic is interesting but difficult.