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Probabilistic Graphical Models 3

Learning

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 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.

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Stanford University

Rating 4.4 based on 20 ratings
Length 6 weeks
Starts Feb 25 (9 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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programming assignments in 4 reviews

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.

Programming assignments are excellent and extremely instructive.

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.

Very informative course videos and challenging yet rewarding programming assignments.

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Coursera

&

Stanford University

Rating 4.4 based on 20 ratings
Length 6 weeks
Starts Feb 25 (9 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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