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.

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
Final exam
18
E
-
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
Final exam
18
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following
6
C
INF/01
Between the years: 1°- 2°
2 modules among the following
6
B
INF/01
Between the years: 1°- 2°
2 modules among the following
6
C
ING-INF/05
6
C
INF/01 ,ING-INF/05
6
C
INF/01
Between the years: 1°- 2°
Further activities: 3 cfu training and 3 cfu further language skill or 6 cfu training. Foreign students must acquire compulsory 3 credits of Italian language skills
6
F
-
Between the years: 1°- 2°

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S010695

Credits

6

Scientific Disciplinary Sector (SSD)

FIS/02 - FISICA TEORICA, MODELLI E METODI MATEMATICI

Learning objectives

The aim of the course is to provide the student with the interdisciplinary physical-mathematical modeling skills for the study of networks of economic agents in interaction, with applications to the characterization of financial markets, business organization, and economic forecasting. Analysis schemes of interacting networks, with particular reference to the spread of agents and influences in the company organization, will in particular be developed. At the end of the course the student has to show to be able to develop analytical-quantitative models and numerical algorithms for the detection of trends in interacting social network systems and for the design of strategies for analyzing and optimizing the business management and dynamics.

Examination methods

To pass the exam, the student must demonstrate: - have understood the physical-mathematical principles underlying the modeling for the study of disordered systems, networks, and recurrent artificial neural networks. - be able to present their arguments in a precise and organic way without digressions - know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
The final exam will include the writing of an essay on one or more topics covered in the course, with subsequent presentation and discussion.