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 Banca e finanza - 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
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2° Year activated in the A.Y. 2025/2026
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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.
Optimal control and machine learning in finance (2025/2026)
Teaching code
4S012449
Academic staff
Coordinator
Credits
9
Language
Italian
Scientific Disciplinary Sector (SSD)
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES
Period
Secondo semestre LM dal Feb 16, 2026 al May 20, 2026.
Courses Single
Authorized
Learning objectives
The course covers two topics. The focus of the first part is on dynamic portfolio optimization, and provides also an introduction to stochastic optimal control. The Merton problem and its generalizations will be studied. Examples of applications of stochastic control in finance will be provided. The second part of the lecture will focus on recent developments in machine learning with applications to finance. We will study supervised learning, deep learning, artificial neural networks and reinforcement learning. Examples in Java and/or Python will be presented.
Prerequisites and basic notions
Students are advised to take this course after Mathematical Finance, Financial Risk Management and Derivatives.
Program
- Introduction to stochastic control through some simple examples
- Discrete-time stochastic control, with examples: theory and applications in Java
- Continuous-time stochastic control, dynamic programming and the HJB equation: theory and application in Java
- Introduction to machine learning: reinforcement learning, supervised learning, deep learning, artificial neural networks - Using neural networks for a "deep hedging" problem : theory and application in Java
Didactic methods
Frontal lessons. Recordings will be made available to students after the lesson.
Learning assessment procedures
Written exam and submission of a project in which the student will have to write a program for the numerical solution of an optimal control problem
Evaluation criteria
As for the written exam, the student is required to demonstrate a critical and in-depth knowledge of the topics covered in the course, both in terms of the more theoretical and the implementation aspects. The answers must be precise and relevant to the questions.
The program must be able to be executed without errors and provide the expected results. It must also follow as much as possible the best practices that we will discuss in the course, and in particular be well documented.
Criteria for the composition of the final grade
The written exam constitutes 75% of the final score, the project the remaining 25%.
Exam language
Inglese
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