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Voltar para Probabilistic Graphical Models 3: Learning

Comentários e feedback de alunos de Probabilistic Graphical Models 3: Learning da instituição Universidade de Stanford

4.6
estrelas
275 classificações
44 avaliações

Sobre o curso

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....

Melhores avaliações

LL

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.

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.

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26 — 44 de 44 Avaliações para o Probabilistic Graphical Models 3: Learning

por Sriram P

Jun 24, 2017

Had a wonderful Experience, Thank you Daphne Ma'am

por 王文君

Jul 30, 2017

Very challenging and fulfilling class!

por 郭玮

Nov 13, 2019

Great course, very helpful.

por Yang P

Jun 20, 2017

Very useful course.

por Alexander K

Jun 04, 2017

Thank You for all.

por Alireza N

Jan 12, 2017

Excellent!

por Shawn

Aug 21, 2020

The course content is great, prof Koller will introduce to you the most useful techniques in PGM and demonstrate algorithms with good examples. This is far more efficient compared to reading a book filled with mathematical expressions, especially if you are new to this area like me

However, I found the ppt could surely need some polishing. During the course 1/2/3, a lot of times you will find that prof Koller speaks about many details and crucial facts that are not even shown in the ppt! Although she wrote down notes sometimes but the notes were hard to recognize (might need a OCR lol), so this is not good if you take screenshot and want to review later.

The 24h cooldown for the final course exam should be reduced at least, if not removed. 4 to 6h is sufficient for a good student to review problem and make correction. And also keep in mind that for the final exams, you don't get to see your answer or any hint about your error once you submit, thus quite challenging and sometimes frustrating, but could be rewarding once you pass.

Among other things, some lectures have bad audio quality, CC are incomplete, and the discussion forums are not very active.

Anyway, if you're about to take the final part of this specialization, good luck!

por Allan J

Mar 04, 2017

Great content. Explores the machine learning techniques with the tightest coupling of statistics with computer science. The Probabilistic Graphical Models series is one of the harder MOOCs to pass. Learners are advised to buy the book and actually read it carefully, preferably in advance of listening to the lectures. The quality of the course is generally high. The discussion is a little muddled at the very end when practical aspects of applying the EM algorithm (for learning when there is missing data) is discussed.

por Diogo P

Nov 15, 2017

Just completed the 3 course specialization. If you're interested (and already have some background) in Machine Learning, this specialization is totally worth it. However, if you have trouble solving any of the quizzes or assignments, do not expect to have any kind of support from the TAs. They simply do not respond to any post in the forum, even if it is related with any bug in the programming assignments source code.

por Niculae I

May 21, 2017

This was a very interesting specialization and beside the theoretical information in the videos I liked very much the programming assignments, which helped very much with understanding more deep the matter. The PAs were also very challenging, especially the ones in the learning part (course 3).

por Vincent L

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.

por Jesus I G R

May 31, 2020

1) The fórums need better assistance.

2) If we could submit Python code por the homework assignments, that would be much better for me.

por Gorazd H R

Jul 07, 2018

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

por Luiz C

Aug 28, 2018

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

por Siwei Y

Feb 03, 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.

por Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

por Ahmed S

Sep 22, 2017

Pros:

The course covers a highly important relatively large set of topics. If you get the content and managed to pass the quizzes and assignments, you're good to go with PGMs.

Cons:

The course is quite old, with no support from neither TAs nor instructors. The material isn't updated to match a specialization (even the assignment numbers are old, some test cases aren't updated and the course content and assignments are quite dependent).

por Rohan M

Dec 05, 2019

Some excellent materials and homeworks, but poor teaching support and poor value for money.

por Jiaxing L

Feb 12, 2017

Managed to be get worse and worse