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 Artificial Intelligence - 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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2 modules among the following (A.A. 2024/2025 Network Science not activated)
1 module among the following
2° Year It will be activated in the A.Y. 2025/2026
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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2 modules among the following (A.A. 2024/2025 Network Science not activated)
1 module among the following
Modules | Credits | TAF | SSD |
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2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)
2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)
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.
Network Science (Not provided 2024/2025)
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.