Linear Methods

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

Content

  • Application - Hebbian learning

  • Perceptron

    • Algorithm

    • Convergence proof

    • Properties

  • Kernel trick

    • Basic idea

    • Kernel Perceptron

    • Kernel expansion

  • Kernel examples

  • Application - Optical Character Recognition

  • Support Vector Machines

    • Large Margin Classification

    • Optimization Problem

    • Dual Problem

  • Properties

    • Support Vectors

    • Support Vector expansion

  • Soft Margin Classifier

    • Noise tolerance

    • Optimization problem

    • Dual problem

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).