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 (2021/2022)
Teaching code
4S009067
Credits
6
Coordinator
Language
Italian
Also offered in courses:
- Statistical learning - PART II of the course Master's degree in Mathematics
The teaching is organized as follows:
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 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)
Program
-- Linear models for Regression (Linear Regression, Subset Variable Selection, Shrinkage/Regularization)
-- 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)
-- Model Assessment and selection (cross validation)
-- Introduction to Neural Networks (Single layer neural network, training a neural network)
Lab:
-- Linear regression and related variable selection/shrinkage methods (in Python)
-- Classification with logistic regression (in Python)
-- Clustering with k-means and hierarchical clustering (in Python)
-- Artificial Neural Networks (in Python)
Examination Methods
The exam consists of an oral test and the realization and presentation of one of the exercises carried out in the laboratory on data analysis using statistical learning methods.
