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.
Type D and Type F activities
Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.
1. Modules taught at the University of Verona
Include the modules listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).
Booklet entry mode: if the teaching is included among those listed below, the student can enter it independently during the period in which the curriculum is open; otherwise, the student must make a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.
2. CLA certificate or language equivalency
In addition to those required by the curriculum/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:
- English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.
Those enrolled until A.Y. 2020/2021 should consult the information found here.
Method of inclusion in the booklet: request the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it
3. Transversal skills
Discover the training paths promoted by the University's TALC - Teaching and learning center intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali
Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.
4. CONTAMINATION LAB
The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.
Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).
Find out more: https://www.univr.it/clabverona
PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.
5. Internship/internship period
In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations): here information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Modules and other activities that can be entered independently in the booklet
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Python programming language | D |
Carlo Combi
(Coordinator)
|
1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
1° 2° | Cooperative Game Theory in the (Deep) RL Era | D |
Alessandro Farinelli
(Coordinator)
|
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).