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Probabilistic Graphical Models 2

Inference

This course is a part of Probabilistic Graphical Models , a 3-course Specialization series from Coursera.

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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Stanford University

Rating 4.1 based on 55 ratings
Length 6 weeks
Starts Jun 29 (3 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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According to other learners, here's what you need to know

programming assignments in 5 reviews

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.

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Coursera

&

Stanford University

Rating 4.1 based on 55 ratings
Length 6 weeks
Starts Jun 29 (3 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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