Basic introduction to state of the rt data analysis and automated classification.
The objective of the course is the acquisition of knowledge of:
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
Teacher's handouts
Teacher's handouts.