Prof. Marta Catalano

Argomenti del programma: In the first part, we will cover linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, subset selection and model regularization methods (ridge and lasso); tree-based methods, random forests and boosting. The focus is on the important elements of modern data analysis and its applications. The computing language is the R programming language.

Argomenti del programma: In the first part, we will cover linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, subset selection and model regularization methods (ridge and lasso); tree-based methods, random forests and boosting. The focus is on the important elements of modern data analysis and its applications. The computing language is the R programming language.

• Hoff P. (2009). A First Course in Bayesian Statistical Methods. Springer.

Argomenti del programma: • Introduction • Probability and Statistics recap • Principles of regression • Simple and multivariate linear regression • Cross-validation • Principles of classification • Logistic regression • Clustering • Principles of Bayesian inference • Bayesian linear regression