machine learning

Decision Trees Tutorial

Written by Andrew Moore, the document is available as PDF.

The Decision Tree is one of the most popular classification algorithms in current use in Data Mining and Machine Learning. This tutorial can be used as a self-contained introduction to the flavour and terminology of data mining without needing to review many statistical or probabilistic pre-requisites. If you're new to data mining you'll enjoy it, but your eyebrows will raise at how simple it all is! After having defined the job of classification, we explain how information gain (next Andrew Tutorial) can be used to find predictive input attributes. We show how applying this procedure recursively allows us to build a decision tree to predict future events. We then look carefully at a question so fundamental, it is the basis for much of all statistics and machine learning theory: how do you choose between a complicated model t
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Neural Networks Tutorial

Tutorial slides by Andrew Moore available as PDF.

We begin by talking about linear regression...the ancestor of neural nets. We look at how linear regression can use simple matrix operations to learn from data. We gurgle with delight as we see why one initial assumption leads inevitably to the decision to try to minimize sum squared error. We then explore an alternative way to compute linear parameters---gradient descent. And then we exploit gradient descent to allow classifiers in addition to regressors, and finally to allow highly non-linear models---full neural nets in all their glory.
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Bayesian Networks Tutorial

Tutorial slides by Andrew Moore available as PDF.

The tutorial first reviews the fundamentals of probability (but to do that properly, please see the earlier Andrew lectures on Probability for Data Mining). It then discusses the use of Joint Distributions for representing and reasoning about uncertain knowledge. Having discussed the obvious drawback (the curse of dimensionality) for Joint Distributions as a general tool, we visit the world of clever tricks involving indepedence and conditional independence that allow us to express our uncertain knowledge much more succinctly. And then we beam with pleasure as we realize we've got most of the knowledge we need to understand and appreciate Bayesian Networks already. The remainder of the tutorial introduces the important question of how to do inference with Bayesian Networks.
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