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.

Type D and Type F activities

The educational activities of type D are chosen by the student, those of type F are further knowledge useful for entering the world of work (internships, soft skills, project works, etc.). According to the Didactic Regulations of the Course, some activities can be chosen and included autonomously in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F educational activities can be covered by the following activities.

1. Teachings taught at the University of Verona.

Include the teachings listed below and/or in the Course Catalogue (which can also be filtered by language of delivery via Advanced Search).

Booklet entry mode: if the teaching is included among those listed below, the student can include it autonomously during the period in which the study plan is open; otherwise, the student must submit a request to the Secretariat, sending the form to carriere.scienze@ateneo.univr.it during the period indicated.

2. CLA certificate or language equivalency.

In addition to those required by the curriculum, the following are recognized:

  • English language: 3 CFUs are recognized for each level of proficiency above the one required by the course of study (if not already recognized in the previous course of study).
  • Other languages and Italian for foreigners: 3 cfu are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).

These cfu will be recognized, up to a maximum of 6 cfu in total, as type F if the teaching plan allows, or as type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.

Booklet entry moderequest the certificate or equivalency to the CLA and send it to the Student Secretariat - Careers for career entry of the exam, via email: carriere.scienze@ateneo.univr.it

3. Soft skills

Discover the training paths promoted by the University's TALC - Teaching and learning center, intended for students regularly enrolled in the academic year of course delivery https://talc.univr.it/it/competenze-trasversali

Booklet entry mode: the teaching is not expected to be included in the curriculum. Only after obtaining the Open Badge, the CFUs in the booklet will be automatically validated. The registration of CFUs in career is not instantaneous, but there will be some technical time to wait.  

4. Contamination lab 

The Contamination Lab Verona (CLab Verona) is an experiential course with modules on innovation and enterprise culture that offers the opportunity to work in teams with students from all areas to solve challenges set by companies and organisations.  

Upon completion of a CLab, students will be entitled to receive 6 CFU (D- or F-type credits).  

Find out more:  https://www.univr.it/clabverona 

PLEASE NOTE: In order to be admitted to any teaching activities, including those of your choice, you must be enrolled in the academic year in which the activities in question are offered. Students who are about to graduate in the December and April sessions are therefore advised NOT to undertake extracurricular activities in the new academic year in which they are not enrolled, as these graduation sessions are valid for students enrolled in the previous academic year. Therefore, students who undertake an activity in an academic year in which they are not enrolled will not be granted CFU credits.  

5. Stage/internship period

In addition to the CFUs required by the curriculum (check carefully what is indicated on the Didactic Regulationshere you can find information on how to activate the internship. 

Check in the regulations which activities can be Type D and which can be Type F.

Please also note that for traineeships activated after 1 October 2024, it will be possible to recognise excess hours in terms of type D credits, limited only to traineeship experiences carried out at host organisations outside the University.

 

Semester 1  From 10/1/24 To 1/31/25
years Modules TAF Teacher
1° 2° Introduction to smart contract programming for ethereum D Sara Migliorini (Coordinator)
1° 2° BEYOND ARDUINO: FROM PROTOTYPE TO PRODUCT WITH STM MICROCONTROLLER D Franco Fummi (Coordinator)
1° 2° APP REACT PLANNING D Graziano Pravadelli (Coordinator)
1° 2° HW components design on FPGA D Franco Fummi (Coordinator)
Semester 2 From 3/3/25 To 6/13/25
years Modules TAF Teacher
1° 2° LaTeX Language D Enrico Gregorio (Coordinator)
1° 2° Rapid prototyping on Arduino D Franco Fummi (Coordinator)
1° 2° Programming Challanges D Romeo Rizzi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Mila Dalla Preda (Coordinator)

Teaching code

4S009023

Credits

6

Coordinator

Not yet assigned

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Courses Single

Authorized

The teaching is organized as follows:

Theory
The activity is given by AI & Robotics - Teoria of the course: Master's degree in Artificial intelligence

Credits

5

Period

Semester 2

Academic staff

Alessandro Farinelli

Laboratory
The activity is given by AI & Robotics - Laboratorio of the course: Master's degree in Artificial intelligence

Credits

1

Period

Semester 2

Academic staff

Daniele Meli

Learning objectives

This course presents the main issues related to Artificial Intelligence techniques for mobile robotic platforms. The objective is to provide the students with the ability to design, apply and evaluate algorithms that allow mobile robotic platforms to interact with the surrounding environment by performing complex tasks with a high level of autonomy. At the end of the course the students must demonstrate to understand the fundamental concepts related to localization, trajectory planning, task planning, decision-making under uncertainty and machine learning in the context of mobile robotic platforms. Moreover, the students must demonstrate to be able to work with the main development tools for mobile robotic applications and to be able to define technical specifications for designing and integrating software modules for mobile robotic platforms. The students must also be able to deal with professional figures to design solutions for the high level control of mobile robotic platforms and to continue the studies independently following the technical evolution in the field of mobile robotics and developing innovative approaches to improve the state of the art.

Program

– Localization and mapping (e.g., recursive state estimation)
– Motion planning for mobile robots (e.g., path planning, obstacle avoidance);
– Decision-making under uncertainty (e.g., Markov Decision Process) .
– Reinforcement learning for mobile robotic platforms (e.g., model-based and model free approaches, Deep RL).
– Lab: implementation of autonomous behaviors for mobile robotic platforms using state of the art development toolkits (e.g., ROS2), simulation environments for empirical evaluation (e.g., Unity), validation on simple mobile platforms (e.g., turtlebot3).

Learning assessment procedures

The exam consists of an oral test focused on the laboratory activities and a second test that can be chosen between two options: i) a project focused on the implementation of some of the techniques studied during the course; ii) an oral exam focused on the topics studied during the course.

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