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

A.A. 2026/2027 A.A. 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.

1° Year

ModulesCreditsTAFSSD
6
B
INF/01
12
B
ING-INF/05
12
B
INF/01

2° Year  It will be activated in the A.Y. 2026/2027

ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
It will be activated in the A.Y. 2026/2027
ModulesCreditsTAFSSD
6
B
INF/01
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
6
B
ING-INF/05
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
INF/01
6
B
ING-INF/05
Between the years: 1°- 2°
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027
6
C
ING-INF/05
6
C
INF/01
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/05
6
C
INF/01
6
C
ING-INF/06
Between the years: 1°- 2°
2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated
6
C
ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/08
6
C
MAT/09
6
C
INF/01 ,ING-INF/05
6
C
INF/01
6
C
FIS/02
6
C
INF/01
6
C
MAT/06
6
C
INF/01
Between the years: 1°- 2°
1 course among the following
6
C
SECS-S/06
6
C
IUS/07
6
C
IUS/02
6
C
M-FIL/02
6
C
IUS/08
6
C
IUS/17
6
C
IUS/04
6
C
IUS/01
Between the years: 1°- 2°
Further activities: 3 CFU training and 3 CFU further language skill or 6 CFU training. International students (i.e. students who do not have an Italian bachelor’s degree) must compulsorily gain 3 CFU of Italian language skills (at least A2 level) and 3 CFU training.
6
F
-
Between the years: 1°- 2°
12
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Machine Learning & Deep Learning (2025/2026)

Teaching code

4S010673

Credits

12

Coordinator

Vittorio Murino

Language

English en

Also offered in courses:

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

FOUNDATION OF MACHINE LEARNING en

Credits

6

Period

1st semester

Academic staff

Cigdem Beyan

Lessons timetable
DEEP LEARNING en

Credits

6

Period

2nd semester

Academic staff

Cigdem Beyan
Vittorio Murino

Lessons timetable

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.

Prerequisites and basic notions

Basic Math
Probability and Statistics
Image Processing (optional)

Bibliography

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Criteria for the composition of the final grade

The final grade will be the average of the grades for the individual modules