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 It will be activated in the A.Y. 2025/2026
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1 module among the following2 modules among the following1 module among the following
- A.A. 2024/2025 Complex systems and social physics - Network science and econophysics - Statistical methods for business intelligence not activated
- A.A. 2025/26 Network science and econophysics not activated1 module among the following2 modules among the followingLegend | 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 (2024/2025)
Teaching code
4S009067
Credits
6
Language
English
Also offered in courses:
- Statistical learning - PART I of the course Master's degree in Artificial intelligence
- Statistical learning - PART II of the course Master's degree in Artificial intelligence
- Statistical learning - PART II of the course Master's degree in Mathematics
- Statistical learning - PART I of the course Master's degree in Data Science
- Statistical learning - PART II of the course Master's degree in Data Science
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Courses Single
Authorized
The teaching is organized as follows:
Parte II
Parte I
Learning objectives
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 data-sets by means of Python software. At the end of the course the students have to demonstrate the following skills: - knowledge of the main stages of data preparation, model creation and evaluation - ability to develop solutions for feature selection - knowledge and ability to use the main regression and regularization models (e.g., LASSO, Ridge Regression) - knowledge and ability to use the main methods for dimensionality reduction (e.g., Principal Component Regression, Partial Least Squares); - knowledge and ability to use the main methods for classification (e.g., KNN, Logistic Regression, LDA) - knowledge and ability to use the main methods for tree-based regression and classification (e.g., decision tree, random forest) - knowledge and ability to use the main methods for unsupervised data analysis (e.g., K-means clustering, hierarchical clustering)
Prerequisites and basic notions
Python programming basics; basics of statistics. Some basic concepts of programming and statistics will be resumed during the course.
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
-- Classification models (Logistic Regression, Linear Discriminant Analysis)
-- Tree Based Methods (Decision Trees, Bagging, Random Forest, Boosting)
-- Unsupervised methods (Principal Component Analysis, K-Means Clustering, Hierarchical Clustering)
-- Introduction to Neural Networks (Single layer neural network, training a neural network)
-- Introduction to methods for time series forecasting
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)
- Methods for time series forecasting
- Artificial Neural Networks (Python)
Bibliography
Didactic methods
Laboratory experiences, lectures, case study.
The rights of students will be preserved in situations of fragile health. In these cases, you are invited to contact the teacher directly to organize the most appropriate remedial strategies.
Learning assessment procedures
The exam consists of an written test on the topics covered in the course and the related exercises carried out in the laboratory. In case of low participation, the written exam will be replaced by an oral exam with equivalent questions. Compatibly with the number of students, the delivery and presentation of a project or a scientific paper on the topics of the course.
Evaluation criteria
Theoretical and applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques taught in the course.
Criteria for the composition of the final grade
The final grade is the average of the grades of part 1 and 2.
Exam language
Inglese
Sustainable Development Goals - SDGs
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