“Introduction to Bayesian Learning” by Hertzmann

  • ©Aaron Hertzmann

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Entry Number: 21

Title:

    Introduction to Bayesian Learning

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Abstract:


    Prerequisites
    Familiarity with linear algebra, calculus, and computer graphics.

    Intended Audience
    Computer graphics researchers and practitioners working on data-driven computer graphics problems, such as animating shape and motion from video or animating from motion capture.

    Description
    Sophisticated computer graphics applications require complex models of appearance, motion, natural phenomena, and even artistic style. Such models are often difficult or impossible to design by hand. Recent research demonstrates that, instead, we can “learn” a dynamical and/or appearance model from captured data, and then synthesize realistic new data from the model. For example, we can capture the motions of a human actor and then generate new motions as they might be performed by that actor. Bayesian reasoning is a fundamental tool of machine learning and statistics, and it provides powerful tools for solving otherwise-difficult problems of learning about the world from data. Beginning from first principles, this course develops the general methodologies for designing learning algorithms and describes their application to several problems in graphics.


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