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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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1 course among the following2 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)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 courses among the following (A.A. 2023/24: Complex systems and Network Science not activated)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 (2023/2024)
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 4, 2024 al Jun 14, 2024.
Courses Single
Authorized
Learning objectives
The goal of this course is to show the relationship between the main methods of machine learning and the standard 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 presented with Bayesian Regression and Gaussian Processes (e.g., prices with Gaussian Processes). Subsequently, an in-depth description of supervised learning will be provided (Feedforward neural networks, convexity and inequality constraints, training validation and testing, stochastic gradient descent and neural networks). Interpretability will then be introduced (restrictions on the design of the neural network, power of neural networks, limits to the variance of the Jacobian). In the final part of the course students will have the opportunity to acquire knowledge on the most important modeling concepts of financial econometrics (as a reference for performance). Autoregressive modeling and fitting time series models (Box) and the Jenkins approach will be presented. Finally, the class of probabilistic models for financial data such as hidden Markov, models with Kalman filter, and particle filtering and its application to stochastic volatility models in finance will be introduced. The course ends with the description of advanced neural networks (recurrent neural networks, convolutional neural networks, autoencoders).
