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
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
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1 module between the following2° Year activated in the A.Y. 2021/2022
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2 modules among the following2 modules among the following1 module between the following| Modules | Credits | TAF | SSD |
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1 module between the following| Modules | Credits | TAF | SSD |
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2 modules among the following2 modules among the following1 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 (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.
