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| 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.
AI and finance (2024/2025)
Teaching code
4S010697
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Period
Semester 2 dal Mar 3, 2025 al Jun 13, 2025.
Courses Single
Authorized
Learning objectives
The goal of this course is to show the relationship between the main classical and machine learning methods for financial econometrics. During the course students will initially be introduced to the fundamentals of Bayesian econometrics (inference, model selection). Then they will be introduced to Bayesian Regression and Gaussian Processes (e.g. pricing and sensitivity with Gaussian Processes). Then they will be given an in-depth description of supervised learning (Feedforward neural networks, convexity and inequality constraints, validation training and testing, stochastic gradient descent). In the second part of the course, students will learn the most important modeling concepts in financial econometrics (as a reference for performance). Autoregressive modeling and calibration of time series models will be presented. This will be followed by the introduction of recurrent neural networks and the comparison of the two approaches and their respective performances. Finally, the Markowitz model for portfolio construction will be introduced and the principle of expected utility optimization explained. After introducing the basics of reinforcement learning, possible applications in this context will be illustrated.
Prerequisites and basic notions
For the course, you need to be familiar with programming in Python as well as with basic mathematics.
Program
During the course students will initially be introduced to the fundamentals of Bayesian econometrics (inference, model selection). Then they will be introduced to Bayesian Regression and Gaussian Processes (e.g. pricing and sensitivity with Gaussian Processes). Then they will be given an in-depth description of supervised learning (Feedforward neural networks, convexity and inequality constraints, validation training and testing, stochastic gradient descent). In the second part of the course, students will learn the most important modeling concepts in financial econometrics (as a reference for performance). Autoregressive modeling and calibration of time series models will be presented. This will be followed by the introduction of recurrent neural networks and the comparison of the two approaches and their respective performances. Finally, the Markowitz model for portfolio construction will be introduced and the principle of expected utility optimization explained. After introducing the basics of reinforcement learning, possible applications in this context will be illustrated.
Bibliography
Didactic methods
The course will be divided into theoretical lectures and lessons devoted to explaining and solving theoretical and programming exercises.
Learning assessment procedures
The final grade will be based on a project to be prepared in small groups and a final exam.
Evaluation criteria
To pass the exam, students must demonstrate:
- to have understood the principles underlying the functioning of artificial intelligence methods for financial applications
- to be able to argue in a precise and organic way, without digressions, on artificial intelligence methodologies for finance
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Criteria for the composition of the final grade
The project and examination contribution to the final grade will be equal.
Exam language
English
