Lectures via slides. The freely available R statistical software will be also used.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Lectures and practical data modeling in R.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.
Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.
Statistical Learning. Estimation of dependences based on empirical data. Supervised and Unsupervised Learning. Regression and Classification problems. Parametric and non-parametric models. Assessing Model Accuracy.
Linear Regression. Simple linear regression. Multiple linear regression. Least squares criterion and parameter estimation. Assessing the accuracy of the coefficient estimates and of the model. Use of qualitative predictors. Extension of the linear model and non-linear relationships.
Classification. Logistic regression; parameter estimation. Linear and quadratic discriminant analysis.
Resampling methods. Cross-validation, Bootstrap.
Linear Model Selection an Regularization. Variable selection. Dimension reduction methods.
Tree-based Methods. Regression Trees and Classification Trees. Bagging, Random Forest, Boosting
Support Vector Machines and Neural Networks. Support vector classifiers. Deep learning and multilayer perceptrons.