1. Introduction to data mining. Tools for data analysis, visualisation and description.
(Lectures 1-3)
2. The linear regression model. Estimation and prediction. (Lectures 4-6)
3.Model selection and evaluation: bias-variance trade-off, model complexity and goodness of fit. Cross-validation. Selection using information criteria. (Lectures 6-9)
4. Regularization and shrinkage methods: rigde regression, lasso, forward stagewise regression. Principal components regression. (Lectures 10-11).
5. Linear methods for classication: Bayes Classication Rule.
Discriminant analysis. Canonical variates. Logistic regression. (Lectures 12-14)
6. Semiparametric regression: Regression splines and smoothing splines. (Lecture 15)
7. Kernel smoothing methods: Local polynomial regression.
Density estimation. Nearest neighbor classication. (Lecture 16)
8. Additive Models, tree-based methods. GAM, Regression and classication trees. Boosting. (Lectures 17-18)
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