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學生對 斯坦福大学 提供的 Probabilistic Graphical Models 1: Representation 的評價和反饋

1,402 個評分


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).


101 - Probabilistic Graphical Models 1: Representation 的 125 個評論(共 304 個)

創建者 Johannes C


necessary and vast toolset for every scientist, data scientist or AI enthusiast. Very clearly explained.

創建者 Alexandru I


Great course. Interesting concepts to learn, but some of them are too quickly and poorly explained.

創建者 Rajmadhan E


Awesome material. Could not get this experience by learning the subject ourselves using a textbook.

創建者 Lucian


Some more exam questions and variation, including explanations when failing, would be very useful.

創建者 Onur B


Great course. Recommended to everyone who have interest on bayesian networks and markov models.

創建者 Elvis S


Great course, looking forward for the following parts. Took it straight after Andrew Ng's one.

創建者 Youwei Z


Very informative. The only drawback is lack of rigorous proof and clear definition summaries.

創建者 Umais Z


Brilliant. Optional Honours content was more challenging than I expected, but in a good way.

創建者 Hao G


Awesome course! I feel like bayesian method is also very useful for inference in daily life.

創建者 Alfred D


Was a little difficult in the middle but the last section summary just refreshed all of it

創建者 Stephen F


This is a course for those interested in advancing probabilistic modeling and computation.

創建者 Una S


Amazing!!! Loved how Daphne explained really complex materials and made them really easy!

創建者 liang c


Great course. and it is really a good chance to study it well under Koller's instruction.

創建者 AlexanderV


Great course, except that the programming assignments are in Matlab rather than Python

創建者 Ning L


This is a very good course for the foundation knowledge for AI related technologies.

創建者 Hong F


Hope there are explanations of the hard questions (marked by *) in the final exam.

創建者 Abhishek K


Difficult yet very good to understand even after knowing about ML for a long time.

創建者 chen h


The exercise is a little difficult. Need to revise several times to fully digest.

創建者 Isaac A


A great introduction to Bayesian and Markov networks. Challenging but rewarding.

創建者 庭緯 任


perfect lesson!! Although the course is hard, the professor teaches very well!!

創建者 Alejandro D P


This and its sequels, the most interesting Coursera courses I've taken so far.

創建者 Naveen M N S


Basic course, but has few nuances. Very well instructed by Prof Daphne Koller.

創建者 Amritesh T


highly recommended if you wanna learn the basics of ML before getting into it.

創建者 Pouya E


Well-structured content, engaging programming assignments in honors track.

創建者 David C


If you are interested in graphical models, you should take this course.