Data mining concepts: Data mining framework, data mining tasks, data mining metrics, data mining techniques, data representation and visualization.

Classification techniques: Introductory concepts, decision trees, rule-based classifier, nearest-neighbor classifier, Bayesian classifier, artificial neural networks, support vector machines, Generalized Linear Discriminant Analysis, Flexible Discriminant Analysis, Penalized Discriminant Analysis, Mixture Discriminant Analysis, Generalized additive models, Multivariate Adaptive Regression Splines (MARS)

Cluster analysis: Basic techniques, density-based cluster, graph-based cluster.

Temporal and spatial mining: prediction, time-series, regression.

Performance evaluation: Receiver Operating Characteristic (ROC) curves, confusion matrix.

Advanced Bayesian Learning Techniques: Bayesian Neural Networks, Boosting and bagging

Applications of data mining: anomaly detection, bioinformatics and medical imaging; Data mining projects; Programming tasks with R/Python