Vowpal Wabbit
An introduction and hands-on demo

Download:

Slides	Presentation (Powerpoint & PDF)	vw.ppt     vw.pdf
Code	Demo - main script (bash)	vw-demo
	Random data-set generator (perl)	random-poly
	Chart density of a single data column (R+ggplot2)	distrib.r
	Chart one numeric column against another (R+ggplot2)	x-vs-y.r

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This is an introduction to supervised machine learning, through a presentation and hands-on demo
of one of the best open-source machine learning tools available today:

        vowpal wabbit

In this talk we'll learn:

• What exactly is machine learning?
• Can we make computers learn from data?
• What's the link between machine learning and optimization?
• When we should use machine learning instead of writing code?
• What makes vowpal wabbit such a great tool for machine learning?
• How can we automatically separate the signal from the noise?
• Will Elmer Fudd befuddle Bugs, or will the damn wabbit win again?

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To run the demo, make sure you have the following prerequisites on your machine:

• vowpal-wabbit (vw)
• R + ggplot2
• perl
• bash

Download the 4 pieces of code from the table above, make sure they are all in the current directory & executable:
Then run:

The whole demo is scripted, so you just need to read the next command step explanation and hit [enter] repeatedly to run the commands.

Developed and tested on Ubuntu Linux.
Should work whereever   bash + R + vw   are available.
You may need to change $ImgViewer from 'gwenview' on non Linux envs to any utility that can display .png files.

Aug 26, 2013 update:

The "tell signal from noise" demo is now an integral part of John Langford's vowpal wabbit master source-tree on github (directory: demo/random-noise).

2015: statically linked precompiled vw binaries for Linux IA64