The virtual course "Graphical Probabilistic Models 1: Representation - Virtual Course - Coursera", is a course with different contents and that offers video classes of Approx. 67 hours to complete. Explore its essential features, and click the orange button to get detailed information on the Coursera e-Learning platform
Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over a large number of interacting random variables.
These representations sit at the intersection of statistics and computer science, drawing on concepts from probability theory, graph algorithms, machine learning, and more.
They are the basis for the most advanced methods in a wide variety of applications, such as medical diagnostics, image understanding, speech recognition, natural language processing, and many, many more.
They are also a fundamental tool in the formulation of many machine learning problems.
This course is the first in a sequence of three.
It describes the two basic PGM representations: Bayesian networks, which are based on a directed graph; and Markov networks, which use an undirected graph.
The course discusses both the theoretical properties of these representations and their use in practice.
The honors track (highly recommended) contains several practical tasks on how to represent some real-world problems.
The course also introduces some important extensions beyond the basic PGM representation, which allow more complex models to be encoded in a compact way.
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