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. 2024/2025
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1 module among the following 2 courses among the following 2 courses among the following (a.a. 2023/24: Statistical methods for business intelligence not activated)2 courses 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 (2023/2024)
Teaching code
4S009067
Credits
6
Coordinator
Language
English
Also offered in courses:
- Statistical learning - PART II of the course Master's degree in Mathematics
- 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
Courses Single
Authorized
The teaching is organized as follows:
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.
Bibliography
Criteria for the composition of the final grade
The final grade is represented by the arithmetic average of the grades of the two parts (1 and 2) of the course.
