Bayesian Networks 1 - Inference - Stanford CS221: AI
Stanford University
60 Courses
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Overview
The course teaches students how to specify joint distributions compactly using Bayesian networks and factor graphs. It covers probabilistic inference, explaining away, consistency of sub-Bayesian networks, and applications such as medical diagnosis, language modeling, object tracking, multiple object tracking, and document classification.
The course aims to equip learners with the skills to model complex systems and make informed decisions using probabilistic programming. The intended audience for this course is individuals interested in artificial intelligence, machine learning, and probabilistic graphical models.
University: Stanford University
Provider: Stanford University
Categories: Artificial Intelligence Courses, Statistics & Probability Courses, Machine Learning Courses
