Learning pattern recognition techniques and application areas. To learn the basics of classification.

Low level signal characterization of pretreatments, signal behavior and properties Simulation and attribute optimization in classifier structure under attribute distribution Defining patterns as a statistical decision problem Bayesian classifiers, artificial neural networks, fuzzy logic Size and data reduction by linear and nonlinear models Statistical learning theories Support vector machines