## Syllabus

EN
IT

### Learning Objectives

The course covers some statistical techniques for supervised and unsupervised learning, plus some methods for machine 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, classification trees, and random forests) 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.

Supervised learning techniques are used to predict a target variable (linear and logistic regression, classification trees, and random forests) 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.

## ALESSIO FARCOMENI

### Prerequisites

-Prerequisite is an introductory statistics and statistical inference course like “Statistical Tools for Decision Making” of the B. A. in Global Governance. Also some math is essential, but only few derivations are made.

### Program

Topic 1: Introduction to R Software

Topic 2: Linear Regression

Topic 3: Logistic Regression

Topic 4: Principal Component Analysis

Topic 5: Cluster Analysis

Topic 6: Machine Learning Methods for Supervised Learning

Topic 7: Modern Applications: Text Mining, Image Processing

Topic 2: Linear Regression

Topic 3: Logistic Regression

Topic 4: Principal Component Analysis

Topic 5: Cluster Analysis

Topic 6: Machine Learning Methods for Supervised Learning

Topic 7: Modern Applications: Text Mining, Image Processing

### Books

Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to

the students by the course instructors during the course.

the students by the course instructors during the course.

### Bibliography

Witten J.D., Hastie T., Tibshirani R. (2014). An Introduction to Statistical Learning with Applications

in R. Springer, Springer Series in Statistics

Chatfield, C. and Collins, A. J. (1981) Introduction to Multivariate Analysis, Chapman & Hall/CRC

Press

Everitt, B. S. and Hothorn, T. (2006) A Handbook of Statistical Analyses Using R. CRC Press.

Available for free at:http://www.ecostat.unical.it/tarsitano/Didattica/LabStat2/Everitt.pdf

Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining,

Inference, and Prediction, Second Edition. Springer, Springer Series in Statistics. Available for

free at: https://web.stanford.edu/~hastie/ElemStatLearn/

in R. Springer, Springer Series in Statistics

Chatfield, C. and Collins, A. J. (1981) Introduction to Multivariate Analysis, Chapman & Hall/CRC

Press

Everitt, B. S. and Hothorn, T. (2006) A Handbook of Statistical Analyses Using R. CRC Press.

Available for free at:http://www.ecostat.unical.it/tarsitano/Didattica/LabStat2/Everitt.pdf

Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining,

Inference, and Prediction, Second Edition. Springer, Springer Series in Statistics. Available for

free at: https://web.stanford.edu/~hastie/ElemStatLearn/

### Teaching methods

In-class teaching.

### Exam Rules

Assessment for attending students will be based on a written exam. This will include closed and open questions. A midterm written exam will be held.

Non attending students will have to take an oral examination in addition to the written exam.

Non attending students will have to take an oral examination in addition to the written exam.

## MARCO STEFANUCCI

### Prerequisites

Prerequisite is an introductory statistics and statistical inference course like “Statistical Tools for Decision Making” of the B. A. in Global Governance. Also some math is essential, but only few derivations are made.

### Books

Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to the students by the course instructors during the course.