Instanced Based Learning

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


  • Parzen windows

    • Basic idea (smoothing over empirical average)

    • Kernels

  • Model selection

    • Overfitting and underfitting

    • Crossvalidation and leave-one-out estimation

    • Bias-variance tradeoff

    • Curse of dimensionality

  • Watson-Nadaraya estimators

    • Regression

    • Classification

  • Nearest neighbor estimator

    • Limit case via Parzen

    • Fast lookup

Supplementary material

Slides available in PDF and Keynote. 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.


This is unedited video straight from a Lumix GH2 with a 12-42mm kit lens and built in microphone (which should explain the sound) … But it should help as a supplement with the slides.