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Alex Smola
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CMU

The Kalman Filter

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

Content

  • Application - robot navigation

  • Linear Dynamical systems

    • Linear dependence

    • Linear observation model

    • Forward smoothing

  • Inference

    • Rauch-Tung-Striebel smoother

    • Parameter inference

    • Gauss elimination

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).