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返回到 Probabilistic Graphical Models 1: Representation

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

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1,380 個評分

課程概述

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....

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ST

2017年7月12日

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!!

CM

2017年10月22日

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).

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76 - Probabilistic Graphical Models 1: Representation 的 100 個評論(共 300 個)

創建者 John P

2022年6月16日

A comprehensive introduction and review of how to represent joint probability distributions as graphs and basic causal reasoning and decision making.

創建者 Vivek G

2019年4月27日

Great course. some programming assignments are tough (not too nicely worded and automatic grader can be a bit annoying) but all in all, great course

創建者 Sureerat R

2018年3月2日

This subject covered in this course is very helpful for me who interested in inference methods, machine learning, computer vision, and optimization.

創建者 Angel G G

2019年12月12日

Great course, I miss some programming assignments (I didn't do the "honors"), but the quizzes are already good to test your general understanding.

創建者 Ayush T

2019年8月23日

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.

創建者 Valeriy Z

2017年11月13日

This course gives a solid basis for the understanding of PGMs. Don't take it too fast. It takes some time to get used to all the concepts.

創建者 Mulang' O

2019年3月31日

I found well structured contend of these rare probabilistic methods (Actually this is the only reasonable course in this approach online)

創建者 Singhi K

2017年8月1日

Not as rigorous as the book, but very good. However, Octave should not be be necessary and is a road block to completing assignments.

創建者 Karam D

2017年4月3日

One of the best courses which i visited.

The explanation was so simple and there were many examples which were so helpful for me

創建者 ALBERTO O A

2018年10月16日

Really well structured course. The contents are complemented with the book. It is a time consuming course. Totally enjoyed!

創建者 Mike P

2019年7月30日

An excellent course, Daphne is one of the top people to be teaching this topic and does an excellent job in presentation.

創建者 Pathirage D

2021年5月29日

one of the best course I have ever followed. by all means it gave thorough understanding of every topic the introduced.

創建者 Matt M

2016年10月22日

Very interesting and challenging course. Now hoping to apply some of the techniques to my Data Science work.

創建者 Samuel d S B

2021年3月13日

Great course. Lectures gives us good intuition on definitions and results. Programming assignments are fun.

創建者 Anton K

2018年5月7日

This was my first experience with Coursera! Thanks prof. Daphne Koller for this course and Coursera at all.

創建者 Kelvin L

2017年8月11日

I guess this is probably the most challenging one in the Coursera. Really Hard but really rewarding course!

創建者 杨涛

2019年3月27日

I think this course is quite useful for my own research, thanks Cousera for providing such a great course.

創建者 HARDIAN L

2018年6月23日

Even though this is the most difficult course I have ever taken in Coursera, I really enjoyed the process.

創建者 Satish P

2020年7月12日

A fantastic course and quite insightful. Require a strong grounding in probability theory to complete it.

創建者 Johannes C

2020年4月19日

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

創建者 Alexandru I

2018年11月25日

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

創建者 Rajmadhan E

2017年8月7日

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

創建者 Lucian

2017年1月15日

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

創建者 Onur B

2018年11月13日

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

創建者 Elvis S

2016年10月28日

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