Lectures via slides. The freely available R statistical software will be also used.
Lectures and practical data modeling in R.
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.
Linear Model Selection an Regularization. Variable selection. Dimension reduction methods.
Support Vector Machines and Neural Networks. Support vector classifiers. Deep learning and multilayer perceptrons.
Mixture models. Mixtures of distributions. Mixtures of regressions.