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
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Ingegneria e scienze informatiche - Enrollment from 2025/2026The 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. 2020/2021
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2 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.
Machine Learning & Pattern Recognition (2019/2020)
Teaching code
4S02803
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
The teaching is organized as follows:
Teoria
Laboratorio
Learning outcomes
The course aims to provide: i) methodological principles underlying the classification; ii) feature selection and extraction techniques; iii) algorithms for supervised and unsupervised learning; parametric and non-parametric parameter estimation; iv) cross-validation techniques for the validation of classifiers. At the end of the course the student should be able to understand if a classification problem can be solved with some existing technology and, in that case, the type of machine learning algorithm that has to be used for the training. Furthermore, the student must demonstrate: i) to understand what kind of characteristics or patterns should be extracted from the raw data coming from a sensor; ii) to understand what kind of classifier should be used in relation with the encountered problem: iii) to understand the complexity of the recognition problem in computational terms; iv) to produce software that recognizes real data; v) be able to use other people's code and modify it adapting it to the problem under examination. This knowledge will allow the student to understand: i) that fit measures guarantee an effective classifier after the phase of his training; ii) what are the techniques for validating the results of a classifier. At the end of the course the student will be able to understand a machine learning or pattern recognition paper.
Program
Introduction: what, what it serves for, systens and applications
Bayes decision theory
Parameter estimation and non-parametric methods
Linear and nonlinear classifiers and discriminant functions
Linear transformations, Fisher method, feature estraction and selection, Principal Component Analysis
Gaussian mixtures and Expectation-Maximization
Kernel methods and Support Vector Machines
Artificial neural networks
Unsupervised learning and clustering
Hidden Markov Models
Examination Methods
Project with report and oral presentation
To be executed with 2 people, 3 people max for more complex projects (to agree with professor)
Oral presentation will also be focused on verifying the knowledge of the course contents
