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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2° Year activated in the A.Y. 2022/2023
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)
2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)
2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)
2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)
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.
Statistical learning - PART II (2021/2022)
Teaching code
4S009067
Teacher
Credits
3
Also offered in courses:
- Statistical learning - PART II of the course Master's degree in Mathematics
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Secondo semestre dal Mar 7, 2022 al Jun 10, 2022.
Learning outcomes
The course aims to introduce students to the statistical models used in data science. The foundations of statistical learning (supervised and unsupervised) will be developed by placing the emphasis on the mathematical basis of the different state-of-the-art methodologies. It also aims to provide rigorous derivations of the methods currently used in industrial and scientific applications to allow students to understand their requirements for correct use. Laboratory sessions will illustrate the use of fundamental algorithms and industrial case studies in which the student will be able to learn to analyze real datasets by means of Python software.
At the end of the course the student has to show to have acquired the following skills:
● knowledge of the main stages of: data analysis and preparation
● ability to use the main regression models
● ability to develop pro-feature selection solutions
● ability to use regularization methods, e.g., ridge regression, LASSO, elastic net, least angle regression, and classification
● knowledge of unsupervised methods
● know and know how to develop algorithms in the field of dimensionality reduction, analysis of the main components (PCA), K-means clustering, hierarchical clustering, and cross-validation
Program
Theory
- Linear models for regression
- Cross-validation
- Variable and model selection in linear regression models
- Regularization for linear regression models
- Methods for dimensionality reduction
Laboratory:
- Introduction to data analysis with Python
- Linear regression (Python)
- Variable and model selection in linear models (Python)
- Ridge and Lasso regularization for linear regression models (Python)
- Classification with logistic regression (Python)
- Data clustering with k-means and hierarchical approaches (Python)
- Artificial Neural Networks (Python)
Examination Methods
The exam consists of an oral test and the delivery and presentation of the code of one of the exercises carried out in the laboratory.