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Probabilistic Graphical Models

Coursera · Stanford University · 4 HN comments

HN Academy has aggregated all Hacker News stories and comments that mention Coursera's "Probabilistic Graphical Models" from Stanford University.
Course Description

InferenceBayesian NetworkBelief PropagationGraphical ModelMarkov Random FieldGibbs SamplingMarkov Chain Monte Carlo (MCMC)AlgorithmsExpectation–Maximization (EM) Algorithm

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This course is offered by Stanford University on the Coursera platform.
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Hacker News Stories and Comments

All the comments and stories posted to Hacker News that reference this url.
I took it a while ago, and it was a ton of work, but I really liked
I took it relatively recently but I have mixed feelings about it. While the topic itself is very interesting and they have some interesting exercises, the explanations in the videos are seriously lacking.

There are big jumps in reasoning and explanations so you'll need to rely on external material quite a bit (I've used Bayesian Reasoning by Barber) Also, exercises are presented in Matlab/Octave which makes them a pain to work with.

After completing the specialization, I've almost felt that you could just do a course that explains this course.

Graphical models[0] & probabilistic programming[1], with the latter making it easier for developers to dive into this growing AI trend. Research in the field for the past decade has been steadily booming with more companies like Microsoft leading the way. I recommend checking out some MOOCs[2] in coursera.




You are in luck. They have just re-released it on Coursera.
That's awesome! Thanks for the link
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