Data mining is defined as the process of "information extraction" from a set of data. In formal terms all data mining problems can be restated as inferring model parameters, consequently we will discuss the issues related both with the choice of good -- or less adequate -- models for a given set of observation and to the problems related with the parameter estimation process given the model.

We will use the framework provided by the machine learning methodology where the emphasis is both on the models and on the type of data and observation process at hand. The illustration of methods is done with real data and realistic observation models.

A short table of contents:
Modelling Data
  Machine Learning
  Latent variable models
Estimation
  Maximum Likelihood
  Maximum a-posteriori
  Bayesian Estimation
  Examples
Unsupervised Estimation models
  General concepts
  Principal Components
  Examples for PCA
  Independent Components
  Examples for ICA
  Mixture Models
  Examples