  A set of introductory notes on the subject of data classification using a
linear classifier and least-squares cost function, and the negative effect of
the presence of outliers on the decision boundary of the linear discriminant.
We also show how a simple scaling could make the outlier less significant,
thereby obtaining a much better decision boundary. We present some numerical
results.
