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

A.A. 2026/2027 A.A. 2025/2026

The 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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
12
B
MAT/05
6
B
MAT/07

2° Year   It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
Final exam
30
E
-
ModulesCreditsTAFSSD
12
B
MAT/05
6
B
MAT/07
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
Final exam
30
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following: area algebra and geometry + analysis
- A.A. 2025/2026 Homological Algebra not activated
- A.A. 2026/2027 Applied algebra not activated
6
B
MAT/03
6
B
MAT/02 ,MAT/03
6
B
MAT/02
6
B
MAT/03
6
B
MAT/02
6
B
MAT/05
6
B
MAT/02
6
B
MAT/05
6
B
MAT/05
Between the years: 1°- 2°
3 modules among the following: area modeling and computational mathematics
6
B
MAT/07
6
B
MAT/08
6
B
MAT/06
6
B
MAT/09
6
B
MAT/08
6
B
MAT/08
6
B
MAT/08
6
B
MAT/06
6
B
MAT/06
6
B
MAT/06
Between the years: 1°- 2°
24 credits among the following courses
- A.A. 2025/2026 Homological Algebra not activated.
- A.A. 2025/2026 Physics education laboratory not activated
- A.A. 2026/2027 Applied algebra not activated
6
C
MAT/01
6
C
MAT/03
6
C
MAT/02 ,MAT/03
6
C
MAT/07
6
C
MAT/02
6
C
MAT/02
6
C
INF/01
6
C
INF/01
6
C
MAT/03
6
C
MAT/08
6
C
MAT/02
6
C
SECS-P/08
6
C
INF/01
6
C
MAT/06
6
C
MAT/01
6
C
MAT/05
6
C
MAT/09
12
C
MAT/04
6
C
MAT/02
6
C
FIS/01
6
C
MAT/08
6
C
MAT/08
6
C
MAT/08
6
C
MAT/05
6
C
MAT/05
6
C
FIS/01
6
C
MAT/06
6
C
MAT/06
6
C
MAT/06
Between the years: 1°- 2°
Further activities
6
F
-
Between the years: 1°- 2°
12
D
-

Legend | 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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Statistical learning (2025/2026)

Teaching code

4S008279

Credits

6

Coordinator

Paolo Dai Pra

Language

English en

Also offered in courses:

Scientific Disciplinary Sector (SSD)

MAT/06 - PROBABILITY AND STATISTICS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Part II
The activity is given by Statistical learning - Part II of the course: Master's degree in Data Science

Credits

3

Period

2nd semester

Academic staff

Alberto Castellini

Lessons timetable
Part I

Credits

3

Period

2nd semester

Academic staff

Paolo Dai Pra

Lessons timetable

Learning objectives

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.

Prerequisites and basic notions

Basics of Mathematical Analysis, Linear Algebra and Probability Calculus

Program

1. Linear regression
Normal random vectors and their properties. Student’s Theorem and its generalization. Linear regression models. Least squares and projections. Parameter estimators. Distribution of the estimators and confidence intervals for the parameters.
2. Model assessment and selection
Loss function; training and prediction error. Comparison between the expected prediction error and the mean SSR for linear regression. Cross validation. Explicit expression of cross validation for linear regression.
3. Regularization
Optimality of the unbiased estimators in linear regression (Gauss-Markov Theorem). Best subset selection. Ridge regression. Quadratic error: the quadratic error in ridge regression is lower than in standard regression for small values of the regularization parameter. LASSO. Principal component analysis.
4. Linear models for classification
Bayes classifier and its optimality. Linear discriminant analysis. Logistic regression. Separating hyperplanes. The perceptron algorithm
5. Tree based models
Tree based models for classification and regression. Bootstrap. Bagging.
6. Clustering.
Center based clustering. K-center clustering; K-median clustering; K-means clustering. Lloyd’s algorithm for K-means. Ward’s algorithm. Spectral clustering: graph Laplacian. The multiplicity of the eigenvalue 0 of the graph Laplacian equals the number of connected components. Spectral clustering algorithm.
7. Introduction to Neural Networks.
Single layer neural networks. Cybenko’s density theorem. Multilayer neural networks. Training a neural network: gradient descent and stochastic gradient descent.

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

All topics will be covered in class. Additional material, such as weekly exercises, notes and further references, will be available on the course Moodle page. Students in situations where travel is restricted due to national provisions or in particular situations of fragility will be specifically protected. In these cases, students are invited to contact the teacher directly to organize the most appropriate recovery methods.

Learning assessment procedures

Oral exam

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The assessment is based on the understanding of the properties and limitations of the methods introduced in the course, and on the control of the related mathematical techniques.

Criteria for the composition of the final grade

The final grade is based on the outcome of the oral exam

Exam language

Inglese