Probabilistic Graphical Models 2
Inference
Probabilistic Graphical Models ,
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 second in a sequence of three. Following the first course, which focused on representation, this course addresses the question of probabilistic inference: how a PGM can be used to answer questions. Even though a PGM generally describes a very high dimensional distribution, its structure is designed so as to allow questions to be answered efficiently. The course presents both exact and approximate algorithms for different types of inference tasks, and discusses where each could best be applied. The (highly recommended) honors track contains two hands-on programming assignments, in which key routines of the most commonly used exact and approximate algorithms are implemented and applied to a real-world problem.
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Rating | 4.1★ based on 62 ratings |
---|---|
Length | 6 weeks |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | Stanford University via Coursera |
Instructor | Daphne Koller |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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What people are saying
programming assignment
I could not submit my programming assignment, and after consulting every available resource, I was not dignified with an answer.
The main issue of this course is that the chaos in the symbol used in the second programming assignment, the lecturer cannot even main self-consistency in the symbol used.
The last programming assignment is not very well designed.
All in all, this class is really great but does not deliver enough content and information in order to be able to solve the programming assignment problems.
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programming assignments
Programming assignments may look a bit archaic, as I see that Matlab/Octave isn't gaining traction in ML community nowadays, but it's a language that's expressive enough, and in no way makes this course boring or worthless.
Unlike other Coursera courses, this specialization covers a lot of conepts accompanied with programming assignments.
I would give it 5 stars just based on the content, but the programming assignments don't work without significant extra effort.
I've got no complaints about the amount of content, but some of concepts were missing and the Programming Assignments were not so well described, sometimes I couldn't understand what to do.
Some say enrollment has dropped off since they began charging for getting access to Quizzes and Programming Assignments.
I'm very happy to support this course financially, as it's loads cheaper than what I'd be paying if I were back at Stanford.Like PGM I, I strongly recommend doing the Honors Programming Assignments, as it's really the way to learn the material well.
The programming assignments are helpful in applying the learned concepts but sometimes it takes long time to figure out what the instruction really means and the code structures.
Unfortunately, the programming assignments are horrible.
Also, I found that there is a big gap between the videos and the programming assignments.
Either the programming assignments get more theoretical explanations, maybe with some examples too, or the videos get more applied than they are now.
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more examples
would like a bit more examples especially regarding the coding.
It would be great to have more examples included in the lectures and slides.
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Rating | 4.1★ based on 62 ratings |
---|---|
Length | 6 weeks |
Starts | Jun 26 (40 weeks ago) |
Cost | $79 |
From | Stanford University via Coursera |
Instructor | Daphne Koller |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Programming Data Science |
Tags | Computer Science Data Science Algorithms Machine Learning |
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