Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Mathematics - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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3 modules to be chosen among the followingTo be chosen betweenLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Statistical methods for data analysis (2018/2019)
Teaching code
4S007624
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
MAT/08 - NUMERICAL ANALYSIS
The teaching is organized as follows:
Statistical modelling
Machine learning
Learning outcomes
The objective is to introduce students to statistical modelling and exploratory
data analysis. The mathematical foundations of Statistical Learning (supervised
and unsupervised learning, deep learning) are developed with emphasis on the
underlying abstract mathematical framework, aiming to provide a rigorous,
self-contained derivation and theoretical analysis of the main models currently
used in applications. Complimentary laboratory sessions will illustrate the use
of both the key algorithms and relevant case studies, mainly by using standard
software environments such as R or Python.
Program
- Introduction to data analysis with R and Python
- Linear methods for regression (linear regression, least squares, MLE: Estimation, Prediction, Tests under Gaussian assumptions, variable/subset selection
- Shrinkage/Regularization methods (Ridge regression, Least absolute shrinkage and selection operator, [Elastic net, Least angle regression])
- Linear methods for classification (Logistic regression, MLE: estimation, prediction, variable selection)
- Linear model assessment and selection (cross-validation, bootstrap methods)
- Clustering analysis (k-means, principal component analysis and spectral clustering)
Bibliography
| Activity | Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|---|
| Machine learning | T. Hastie, R. Tibshirani, J. Friedman. | The elements of statistical learning. Data mining, inference, and prediction. (Edizione 2) | Springer | 2009 |
Examination Methods
The purpose of the exam is to evaluate the capabilities of the student to understand and use the methodologies presented in the course. The exam consists of a project assignment about specific case studies. Alternatively, the student may choose to give a public presentation about advanced methodologies from the literature related to the topics of the course.
