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.

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 2025/2026

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.

CURRICULUM TIPO:

1° Year 

2° Year   activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
Final exam
24
E
-
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Between the years: 1°- 2°
Between the years: 1°- 2°
Further activities
3
F
-
Between the years: 1°- 2°
3
F
L-LIN/12

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S008902

Credits

6

Coordinator

Marco Cristani

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

The teaching is organized as follows:

Teoria

Credits

5

Period

Secondo semestre

Academic staff

Marco Cristani

Laboratorio

Credits

1

Period

Secondo semestre

Academic staff

Marco Cristani

Learning outcomes

The course aims to provide the theoretical foundations and describe the main methodologies relating to the machine learning area. In particular, the course will deal with the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are the basis, are used, and often complete many other disciplines and application areas of wide diffusion, such as computational vision, robotics, image processing, data mining, analysis and interpretation of medical and biological data, bioinformatics, biometrics, video surveillance, speech and text recognition and many others. More precisely, the methodologies that will be introduced in the course are often an integral part of the aforementioned application areas, and constitute the "intelligent" part with the final objective of understanding (classifying, recognizing, analyzing) the data coming from the process of interest ( whether they are signals, images, strings, categorical, or other types). Starting from the type of measured data, the entire analysis pipeline will be considered such as the extraction and selection of characteristics; supervised and unsupervised machine learning methods, parametric and non-parametric analysis techniques, and validation protocols. Finally, the recent deep learning techniques will be analyzed in general with some case studies. In conclusion, the course aims to provide the student with a set of theoretical foundations and algorithmic tools to address the problems that can be encountered in strategic and innovative industrial sectors such as those involving the processing of large amounts of data (big data), multimedia, visual inspection of products and automation in general.

Program

The course can be divided into two parts, the methodology and the application, which go hand in hand during the course.

Methodologies
- Introduction
- Recognition and classification
- Bayesian Decision Theory
- Parameters Estimation
- Nonparametric Methods of Parameters Estimation
- Linear and non-linear discriminant functions
- Extraction and feature selection, PCA, Fisher transform
- Expectation-Maximization Algorithm on mixtures of Gaussians
- Generative and discriminative methods
- Kernel Methods and Support Vector Machines
- Hidden Markov Models
- Methods for unsupervised classification (clustering)
- Pattern recognition for the analysis and recognition in images and videos

Applications
- Face recognition
- Tracking
- Video surveillance

Textbooks:
- Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Examination Methods

Oral Exam:
the oral discussion will be composed on two parts: theory + lab
The discussion on the lab part will be focused on a small code project – homework, and precisely on how the homework has been carried out. The homework will be chosen by the student among a list of topics.
The discussion on the theory part will be focused on two topics.
Both the parts of the exam will be discussed the same day, individually, and have the same weight on the final grade.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE