Basic introduction to state of the art data analysis and automated classification.
The objective of the course is the acquisition of knowledge of:
Frontal lectures in classroom
Laboratory lectures
Data visualization, descriptive statistics
regression and correlation. Logistic regression.
Bayes approach to learning. MAP.
TS, CS, trainign and generalization error. Confuson matrix. ROC.
LDA, SVM.
Kernel trick: non linear SVM
PCA, non linear techniques for dimension reduction
K-nn
CART.
Clustering: k-means and hierarchical clustering.
Ensblem techniques. Boosting
The objective of the course is the acquisition of knowledge of:
Tutorial on line, handsout
Teacher's handouts.