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

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
6
B
MAT/07
6
B
MAT/03
12
B
MAT/05
6
B
MAT/05
6
B
MAT/06

2° Year   It will be activated in the A.Y. 2025/2026

ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
It will be activated in the A.Y. 2025/2026
ModulesCreditsTAFSSD
6
B
MAT/05
Final exam
32
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
1 module between the following:
- A.A. 2024/2025 Computational algebra not activated;
- A.A. 2025/2026 Homological Algebra not activated.
6
B
MAT/02
6
B
MAT/02
Between the years: 1°- 2°
1 module between the following
6
B
MAT/08
6
B
MAT/08
Between the years: 1°- 2°
3 modules among the following
6
C
MAT/03
6
C
MAT/02 ,MAT/03
6
C
MAT/02
6
C
MAT/08
6
C
MAT/08
6
C
MAT/02
6
C
MAT/06
6
C
INF/01
6
C
MAT/05
6
C
MAT/09
6
C
MAT/02
6
C
MAT/08
6
C
MAT/08
6
C
MAT/07
6
C
INF/01 ,MAT/06
Between the years: 1°- 2°
12
D
-
Between the years: 1°- 2°
Further activities
4
F
-

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 (2024/2025)

Teaching code

4S008279

Credits

6

Coordinator

Paolo Dai Pra

Language

English en

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

PART I en

Credits

3

Period

Semester 2

Academic staff

Paolo Dai Pra

Lessons timetable
PART II en

Credits

3

Period

Semester 2

Academic staff

Alberto Castellini

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

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.

Criteria for the composition of the final grade

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