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.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| primo semestre (lauree magistrali) | Oct 4, 2021 | Dec 17, 2021 |
| secondo semestre (lauree magistrali) | Feb 21, 2022 | May 13, 2022 |
| Session | From | To |
|---|---|---|
| sessione invernale | Jan 10, 2022 | Feb 18, 2022 |
| sessione estiva | May 23, 2022 | Jul 8, 2022 |
| sessione autunnale | Aug 22, 2022 | Sep 23, 2022 |
| Session | From | To |
|---|---|---|
| sessione autunnale (validità a.a. 2020/2021) | Dec 6, 2021 | Dec 10, 2021 |
| sessione invernale (validità a.a. 2020/2021) | Apr 6, 2022 | Apr 8, 2022 |
| sessione estiva (validità a.a. 2021/2022) | Sep 5, 2022 | Sep 6, 2022 |
Exam calendar
Exam dates and rounds are managed by the relevant Economics Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrollment FAQs
Academic staff
045 802 8224
maurizio.malpede@univr.it
Study Plan
The 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 |
|---|
One module between the following2° Year activated in the A.Y. 2022/2023
| Modules | Credits | TAF | SSD |
|---|
Two modules among the followingTwo modules among the followingOne module between the following| Modules | Credits | TAF | SSD |
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One module between the following| Modules | Credits | TAF | SSD |
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Two modules among the followingTwo modules among the followingOne module between the following| Modules | Credits | TAF | SSD |
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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.
Machine Learning for Economics (2022/2023)
Teaching code
4S008979
Academic staff
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
SECS-S/01 - STATISTICS
Period
Secondo semestre (lauree magistrali) dal Feb 20, 2023 al May 19, 2023.
Learning objectives
The goal of the course is to provide students with mathematical, statistical and computational tools for a rigorous understanding of machine learning. A central aspect is the critical discussion of how and to which extent machine learning methods are essential in large scale data analysis in order to develop a professional profile combining solid quantitative skills with an in-depth knowledge of economic and corporate dynamics to support strategic decisions based on data analysis. At the end of the course students will be able to master classical methods of machine learning, implement data analysis algorithms, choose the most suitable techniques, identify relevant structures underlying the data for prediction purposes, critically discuss the output generated by a machine learning technique.
Prerequisites and basic notions
The knowledge provided in the basic courses of statistics and econometrics is assumed.
Program
- Overview of Statistical Learning
- Linear Regression Models and Least Squares
• The Gauss-Markov Theorem
• Best-Subset Selection
• Shrinkage Methods: Ridge Regression and the Lasso
- Linear Methods for Classification
• Bayes classifier
• Linear Discriminant Analysis
• Logistic Regression
- Model Assessment and Selection
• Bias-Variance and Model Complexity
• Cross-Validation
- Introduction to Neural Networks
• Neural Networks
• Fitting Neural Networks
- Clustering Methods
Bibliography
Didactic methods
The course includes 36 hours of frontal teaching, of which 24 hours of lessons (equal to 4 CFU) and 12 hours of lab sessions (equal to 2 CFU).
Learning assessment procedures
The exam will test for
(a) the understanding of the theoretical tools (concepts and formal models) presented in the course,
(b) the ability to use theoretical tools to discuss results from a data set analysis.
The final exam will consist of two parts:
- a written exam on the material of the theoretical lectures and the lab sessions. At the end of the course one general
assignment will be given to be delivered before the oral exam on a date to be communicated later on,
- an oral test on the theoretical lectures of the course.
Evaluation criteria
The theory part of the written test has a weight of 2/3, while the part on the use of the software has a weight of 1/3.
Criteria for the composition of the final grade
The final grade of the exam results from the arithmetic average of the marks of the written and oral tests.
Exam language
Inglese
Type D and Type F activities
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | The fashion lab (1 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| 1° 2° | The fashion lab (2 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| 1° 2° | The fashion lab (3 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Marketing plan | D |
Virginia Vannucci
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
(Coordinator)
|
| 1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
(Coordinator)
|
| 1° 2° | Internationalization and Sustainability. Friends or Enemies? | D |
Angelo Zago
(Coordinator)
|
| 1° 2° | Data Analysis Laboratory with R (Verona) | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Data Visualization Laboratory | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Python Laboratory | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Data Science Laboratory with SAP | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Advanced Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Excel Laboratory (Verona) | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Programming in Matlab | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Programming in SAS | D |
Marco Minozzo
(Coordinator)
|
| 1° 2° | Samsung Innovation Camp | D |
Marco Minozzo
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Business & Predictive Analytics for International Firms (with Excel Applications) - 2021/2022 | D |
Angelo Zago
(Coordinator)
|
| 1° 2° | What paradigms beyond the pandemic? Individual vs. Society, Private vs. Public | D |
Federico Brunetti
(Coordinator)
|
| 1° 2° | Data Discovery for Business Decisions- 2021/2022 | D |
Claudio Zoli
(Coordinator)
|
| 1° 2° | Elements of Financial Risk Management - 2021/2022 | D |
Claudio Zoli
(Coordinator)
|
| 1° 2° | English for business and economics | F |
Claudio Zoli
|
| 1° 2° | Integrated Financial Planning | D |
Riccardo Stacchezzini
(Coordinator)
|
| 1° 2° | Introduction to Business Plan-2021/2022 | D |
Paolo Roffia
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | The fashion lab (1 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| 1° 2° | The fashion lab (2 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| 1° 2° | The fashion lab (3 ECTS) | D |
Caterina Fratea
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | La metodologia SEM applicata allo studio della relazione tra gestione del rischio e performance nelle PMI | D |
Cristina Florio
(Coordinator)
|
| 1° 2° | Laboratory on research methods for business | D |
Cristina Florio
(Coordinator)
|
| 1° 2° | Professional Communication for Economics A.A. 2021-22 | D |
Claudio Zoli
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | How to Enter in a Foreign Market. Theory and Applications - 2021/2022 | D |
Angelo Zago
(Coordinator)
|
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Graduation
List of thesis proposals
| theses proposals | Research area |
|---|---|
| PMI (SMES) and financial performance | MANAGEMENT OF ENTERPRISES - MANAGEMENT OF ENTERPRISES |
| Corporate governance, financial performance and international business | Various topics |
Linguistic training CLA
Internships
The curriculum of the three-year degree courses (CdL) and master's degree courses (CdLM) in the economics area includes an internship as a compulsory training activity. Indeed, the internship is considered an appropriate tool for acquiring professional skills and abilities and for facilitating the choice of a future professional outlet that aligns with one's expectations, aptitudes, and aspirations. The student can acquire further competencies and interpersonal skills through practical experience in a work environment.
Gestione carriere
Student login and resources
Methods of teaching delivery
All lectures as well as all the exams are held in person. In particular, we highlight the importance of taking part in classroom activities in order to benefit from interaction with colleagues and instructors and participating in project works, presentations and group works that could be organized by the different courses.
Furthermore, as a further service to students, the lessons will be video-recorded and made available on the relevant e-learning platform of the courses unless otherwise communicated by the individual lecturers who will also define the methods and times for activating this service. However, it is underlined that the recordings do not represent a substitute for the lectures and activities carried out in the classroom.
