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