Argomenti dell'insegnamento
M1: - Review of statistical inference: random variables, confidence intervals, and hypothesis testing. - Introduction to statistical learning concepts: basic vocabulary and notions, parametric and nonparametric approaches, predictive and inferential objectives, bias-variance trade off, supervised and unsupervised learning - Linear regression and extensions: simple and multiple linear regression, model estimation and assessment, model assumptions, inferential tools, qualitative predictors, interaction effects, polynomial regression, basic notions on nonparametric regression - Classification: introduction to classification, logistic regression, model estimation, evaluation of classifiers - Other supervised learning techniques: trees, splines, additive models - Model selection/assessment and evaluation of model complexity: resampling methods, cross-validation and information criteria - Unsupervised learning: clustering tools such as k-means and hierarchical clustering, principal component analysis - Applications with the R software M2: - Parameter estimation: maximum likelihood methods - Parameter estimation: Bayesian inference - Time series: components and forecasting - Time series: causal relationship tests - Missing data - Elements of statistics for Big Data

Modalità di insegnamento
M1: In-person lectures and computer labs. Whenever possible, lectures will be structured to prioritize in-class time for discussions, and practical applications. M2: Frontal lectures, discussions and exercises on computer.

Modalità d'esame
The overall exam mark will be determined by the assessment of the two modules (M1+M2). M1: Assessment (for both attending and non-attending students): - Written Exam: Exercises and review questions (65% of the final grade). - Data Analysis Project: Group project in which students select and analyze an interesting dataset using the tools learned in the course. Groups will present their work at the end of the course (35% of the final grade; optional). Notes: - For students who do not complete the project, the written exam will count for 100% of the final grade. - Project grades remain valid for one academic year. M2: The assessment is based on class and lab participation, home-work exercises and a final written exam. The final written exam will include open questions and exercises to be worked out by the students as well as computational exercises to be solved with R.

Criteri di valutazione
M1: - Written exam: understanding of statistical concepts, correct interpretation of results of statistical analyses, clarity and precision of explanations. - Data Analysis Project: Quality and clarity of the presentation, adequacy and appropriateness of analyses with respect to dataset characteristics M2: For attending students the final grade will be determined by the evaluation of homeworks, class and lab participation (20%) and the evaluation of a final written exam (80%). The homeworks and the final written exam are separately evaluated with a score expressed in 30/30. For non-attending students the final grade will be determined by the evaluation of a final written exam (100%). The final written exam is evaluated with a score expressed in 30/30.