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Alex Smola
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Gaussian Processes

Introduction to Machine Learning - 10-701/15-781

Content

  • Motivation - estimating height & weight

  • Gaussian Process

    • Mean function

    • Covariance function

    • Relation to kernels

  • Gaussian Process regression

    • Jointly normal distribution

    • Adding noise

    • Schur complement

  • Logistic regression

    • Generative model

    • Connection to SVMs

Supplementary material

Slides in PDF and Keynote coming soon. If you want to extract the equations from the slides you can do so by using LaTeXit, simply by dragging the equation images into it.

Videos

This is unedited video straight from a Lumix GF2 with a 20mm lens which should explain the sound (it doesn't have a dedicated audio input) … But it should help as a supplement with the slides (YouTube typically makes the 1080i version available within 1 week of the upload).