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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
232 classificações
33 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 — 33 de {totalReviews} Avaliações para o Probabilistic Graphical Models 3: Learning

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 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 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 Michel S

Jul 14, 2018

Good course, but the material really needs a refresh!

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 Amine M

Jun 17, 2019

Great lectures and terrible assignments. Forum is not helpful at all. In fact, the forum is dead and tutors do not exist. Programming assignments have too many errors which are known within the forum for 4 years but no one is fixing these mistakes. All in all, the topic is highly interesting but the implementation is deficient

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 Jiaxing L

Feb 12, 2017

Managed to be get worse and worse