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
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 Economics and data analysis - Enrollment from 2025/2026years | 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)
|
Machine Learning for Economics (2021/2022)
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 21, 2022 al May 13, 2022.
Learning outcomes
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.
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
Textbooks and references:
Lecture notes and references to the textbooks chapters will be made available on the e-learning web page.
Bibliography
Examination Methods
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 lab sessions. During the course candidates will have the opportunity to
solve two partial assignments that will be part of the final evaluation. Alternatively, there will be one general
assignment due before the oral exam on a date to be communicated later on,
- an oral test on the theoretical lectures of the course.