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 Artificial Intelligence - 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.

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
Final exam
18
E
-
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
Final exam
18
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among the following
6
C
INF/01
Between the years: 1°- 2°
2 modules among the following
6
B
INF/01
Between the years: 1°- 2°
2 modules among the following
6
C
ING-INF/05
6
C
INF/01 ,ING-INF/05
6
C
INF/01
Between the years: 1°- 2°
Further activities: 3 cfu training and 3 cfu further language skill or 6 cfu training. Foreign students must acquire compulsory 3 credits of Italian language skills
6
F
-
Between the years: 1°- 2°

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

4S010673

Credits

12

Coordinator

Marco Cristani

Language

English en

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

The teaching is organized as follows:

Deep Learning - Teoria
The activity is given by Deep Learning - Teoria of the course: Master's degree in Computer Engineering for Robotics and Smart Industry

Credits

5

Period

Semester 1

Academic staff

Marco Cristani

Deep Learning - Laboratorio
The activity is given by Deep Learning - Laboratorio of the course: Master's degree in Computer Engineering for Robotics and Smart Industry

Credits

1

Period

Semester 1

Academic staff

Marco Cristani

Foundation of Machine Learning - Teoria
The activity is given by Fundamentals of Machine Learning - Teoria of the course: Master's degree in Computer Science and Engineering

Credits

5

Period

Semester 2

Academic staff

Marco Cristani

Foundation of Machine Learning - Laboratorio
The activity is given by Fundamentals of Machine Learning - Laboratorio of the course: Master's degree in Computer Science and Engineering

Credits

1

Period

Semester 2

Academic staff

Marco Cristani

Learning objectives

The course aims to provide the theoretical foundations and describe the main methodologies relating to the area of machine learning, together with the most recent techniques of deep learning. In particular, the course will deal with describing the methods of analysis, recognition and automatic classification of data of any type, typically called patterns. These disciplines are at the base, are used, and often complement 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, forecasting. More precisely, the methodologies that will be introduced in the course are often an integral part of the application areas mentioned above, and constitute the "intelligent" part of it with the final aim of understanding (classifying, recognizing, analyzing) the data coming from the process of interest (be they signals, images, strings, categorical, or other types). Starting from the type of data measured, 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, together with visualization necessary for understanding deep learning systems. At the laboratory level, real case studies and not just academic benchmarks will be presented, addressed with appropriate programming tools. In conclusion, the course aims to provide the student with a set of theoretical foundations and algorithmic tools to address the problems that may 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, automation and forecasting.

Learning assessment procedures

The exam involves the discussion of a code project, which proposes a solution to an industrial classification problem. The final score will depend on the classification figure of merits achieved by the classifier and the theoretical motivations that prompted the student to choose a particular algorithm.

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