Bachelor Degree in Global Governance

Updated A.Y. 2021-2022

Course Description

The course covers some statistical techniques for supervised and unsupervised learning. The R software for statistical computing will be also introduced and used throughout.

Supervised learning techniques are used to predict a target variable (linear and logistic regression) based on predictors, and/or to assess interrelationships among predictors and a target variable (linear and logistic regression). As an example, suppose you want to predict the risk that a family will be materially deprived next year. This can be done by using data that can be measured at baseline (number of family members, disposable income, health status, etc.) and use these to predict material deprivation for a sample of families with known status. Incidentally, you will also understand how health status affects the risk of material deprivation.

Unsupervised learning techniques are used to find groups in data, that is, to predict target categorical variables that are not measured (cluster analysis). Additionally, they are used to summarize data (dimension reduction, done with principal component analysis in this course). As an example, suppose you want to assess an unmeasurable trait, like happiness. Suppose your target units are geographic regions. Happiness can be measured indirectly through a series of variables (questionnaires, indices, etc.). A general score is obtained through dimension reduction by finding the optimal weighted average of all measurements. Cluster analysis will separate regions in few (two, three, four) groups, with respect to levels of happiness. Different policies can then be scheduled for each group. The last 3 CFU will be dedicated to machine learning methods (classification and regression trees, random forests, shallow and deep neural networks) for supervised learning. Modern applications will be then introduced, where data is extracted from text corpora (natural language processing), images (computer vision), audio tracks.

The main objectives of this course are to provide students with the ability to select the statistical learning technique needed to answer specific questions (based on data), to perform data analysis appropriately, and to interpret the results correctly.

Find more information in the Syllabus

Updated A.Y. 2020-2021

Course Description

The course covers some statistical techniques for supervised and unsupervised learning. The R software for statistical computing will be also introduced and used throughout.

Supervised learning techniques are used to predict a target variable (linear and logistic regression) based on predictors, and/or to assess interrelationships among predictors and a target variable (linear and logistic regression). As an example, suppose you want to predict the risk that a family will be materially deprived next year. This can be done by using data that can be measured at baseline (number of family members, disposable income, health status, etc.) and use these to predict material deprivation for a sample of families with known status. Incidentally, you will also understand how health status affects the risk of material deprivation.

Unsupervised learning techniques are used to find groups in data, that is, to predict target categorical variables that are not measured (cluster analysis). Additionally, they are used to summarize data (dimension reduction, done with principal component analysis in this course). As an example, suppose you want to assess an unmeasurable trait, like happiness. Suppose your target units are geographic regions. Happiness can be measured indirectly through a series of variables (questionnaires, indices, etc.). A general score is obtained through dimension reduction by finding the optimal weighted average of all measurements. Cluster analysis will separate regions in few (two, three, four) groups, with respect to levels of happiness. Different policies can then be scheduled for each group. The last 3 CFU will be dedicated to machine learning methods (classification and regression trees, random forests, shallow and deep neural networks) for supervised learning. Modern applications will be then introduced, where data is extracted from text corpora (natural language processing), images (computer vision), audio tracks.

The main objectives of this course are to provide students with the ability to select the statistical learning technique needed to answer specific questions (based on data), to perform data analysis appropriately, and to interpret the results correctly.

Find more information in the Syllabus