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.
Academic calendar
The academic calendar shows the deadlines and scheduled events that are relevant to students, teaching and technical-administrative staff of the University. Public holidays and University closures are also indicated. The academic year normally begins on 1 October each year and ends on 30 September of the following year.
Course calendar
The Academic Calendar sets out the degree programme lecture and exam timetables, as well as the relevant university closure dates..
| Period | From | To |
|---|---|---|
| Semester 1 | Oct 2, 2023 | Jan 26, 2024 |
| Semester 2 | Mar 4, 2024 | Jun 14, 2024 |
| Session | From | To |
|---|---|---|
| Winter exam session | Jan 29, 2024 | Mar 1, 2024 |
| Summer exam session | Jun 17, 2024 | Jul 31, 2024 |
| Autumn exam session | Sep 2, 2024 | Sep 30, 2024 |
| Session | From | To |
|---|---|---|
| Summer graduation session | Jul 19, 2024 | Jul 19, 2024 |
| Autumn graduation session | Oct 21, 2024 | Oct 21, 2024 |
| Winter graduation session | Mar 26, 2025 | Mar 26, 2025 |
| Period | From | To |
|---|---|---|
| Festa di Ognissanti | Nov 1, 2023 | Nov 1, 2023 |
| Festa dell'Immacolata | Dec 8, 2023 | Dec 8, 2023 |
| Vacanze di Natale | Dec 24, 2023 | Jan 7, 2024 |
| Festività pasquali | Mar 29, 2024 | Apr 1, 2024 |
| Ponte della Festa della Liberazione | Apr 25, 2024 | Apr 26, 2024 |
| Festa del Lavoro | May 1, 2024 | May 1, 2024 |
| Festività del Santo Patrono: San Zeno | May 21, 2024 | May 21, 2024 |
| Festa della Repubblica | Jun 2, 2024 | Jun 2, 2024 |
| Vacanze estive | Aug 12, 2024 | Aug 17, 2024 |
Exam calendar
Exam dates and rounds are managed by the relevant Science and Engineering Teaching and Student Services Unit.
To view all the exam sessions available, please use the Exam dashboard on ESSE3.
If you forgot your login details or have problems logging in, please contact the relevant IT HelpDesk, or check the login details recovery web page.
Should you have any doubts or questions, please check the Enrollment FAQs
Academic staff
+39 045 802 7913
bernardo.calabrese@univr.it
ariel.caputo@univr.it
mila.dallapreda@univr.it
stefano.gatti@univr.it
pasquale.mirtuono@univr.it
diego.tilola@univr.it
claudio.tomazzoli@univr.it
rosamaria.vadala@univr.it
Study Plan
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
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 course among the following2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)2 courses among the following (A.A. 2023/24: Complex systems and Network Science not activated)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.
Reinforcement learning and Advanced programming for AI (2023/2024)
Teaching code
4S010675
Credits
12
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Courses Single
Authorized
The teaching is organized as follows:
Advanced programming for AI
Reinforcement Learning
Learning objectives
The reinforcement learning module introduces students to reinforcement learning and planning under uncertainty. In particular, it is focused on the design of algorithms that enable machines to learn based on reinforcements, hence from partial, implicit and delayed feedback obtained by repeatedly interact with the environment or users. At the end of the course, students will have to demonstrate that they have acquired the ability to i) tackle sequential decision problems with reinforcement learning techniques, ii) identify and apply the most effective and efficient algorithms to solve specific sequential decision problems, iii) designing new reinforcement learning algorithms. In particular, the acquired knowledge concerns advanced techniques for the resolution of Markov Decision Process (eg, research with Monte Carlo methods), bandit problems, model-based and model-free reinforcement learning, Bayesian reinforcement learning, deep reinforcement learning, and advanced reinforcement learning techniques (safe policy improvement, partially observable environments, hierarchical reinforcement learning, imitation-based learning, inverse reinforcement learning, and meta-learning). The Advanced Programming for AI module aims to provide information on programming languages, tools and software architectures that have emerged in the field of software systems (SW) based on artificial intelligence. The goal is to provide students with an understanding of the specific characteristics and key principles underlying various languages and advanced tools and to solve some classes of AI problems. Students will acquire programming skills in Python, will be able to write programs to solve typical problems and to assemble software modules, manage models, patterns, and perform the deployment of those modules on cloud platforms, with particular focus on interoperability and explainability.
Prerequisites and basic notions
Being a first year, first semester exam, there are no specific prerequisites other than those required for access to the degree course.
Program
REINFORCEMENT LEARNING
- Markov Decision Process
- Research with Monte Carlo methods
- Bandit problems
- Model-based and model-free reinforcement learning
- Bayesian reinforcement learning
- Deep reinforcement learning
- Advanced reinforcement learning techniques
--- safe policy improvement
--- partially observable environments
--- hierarchical reinforcement learning
--- imitation-based learning
--- inverse reinforcement learning
--- meta-learning
ADVANCED PROGRAMMING FOR AI
Bibliography
Didactic methods
Lectures, laboratory experiences, exercises.
Learning assessment procedures
To pass the exam, students will have to demonstrate that they: - understand the principles behind how reinforcement learning and methods for programming modules based on artificial intelligence work - be able to expose concepts of reinforcement learning and programming of modules based on artificial intelligence in a precise and organic way without digressions, - knowing how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
The exam consists of an oral test on the topics covered in the course. In case of high participation, the oral exam will be replaced by a written exam with equivalent questions. The questions may concern both the theoretical part and the exercises carried out in the laboratory.
Evaluation criteria
Theoretical and applied knowledge of the techniques taught in the course; critical ability to select techniques based on the problem; ability to use the techniques taught in the course.
Criteria for the composition of the final grade
The final grade is represented by the arithmetic average of the grades of the two parts (RL / Advanced programming for AI) of the course.
Exam language
Inglese (English)
Sustainable Development Goals - SDGs
This initiative contributes to the achievement of the Sustainable Development Goals of the UN Agenda 2030. More information on sustainability
Type D and Type F activities
Type D learning activities are the student's choice, type F activities are additional knowledge useful for job placement (internships, transversal skills, project works, etc.). According to the Teaching Regulations of the Course, some activities can be chosen and entered independently in the booklet, others must be approved by a special committee to verify their consistency with the study plan. Type D or F learning activities can be covered by the following activities.
1. Modules taught at the University of Verona
Include the modules 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 enter it independently during the period in which the curriculum is open; otherwise, the student must make 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/study plan, the following are recognized for those matriculated from A.Y. 2021/2022:
- English language: 3 CFUs are recognized for each level of proficiency above that required by the course of study (if not already recognized in the previous course of study).
- Other languages and Italian for foreigners: 3 CFUs are recognized for each proficiency level starting from A2 (if not already recognized in the previous study cycle).
These CFUs will be recognized, up to a maximum of 6 CFUs in total, of type F if the study plan allows it, or of type D. Additional elective credits for language knowledge may be recognized only if consistent with the student's educational project and if adequately justified.
Those enrolled until A.Y. 2020/2021 should consult the information found here.
Method of inclusion in the booklet: request the certificate or equivalency from CLA and send it to the Student Secretariat - Careers for the inclusion of the exam in the career, by email: carriere.scienze@ateneo.univr.it
3. Transversal 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
Mode of inclusion in the booklet: the teaching is not expected to be included in the curriculum. Only upon obtaining the Open Badge will the booklet CFUs 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. Internship/internship period
In addition to the CFUs stipulated in the curriculum/study plan (check carefully what is indicated on the Teaching Regulations): here information on how to activate the internship.
Check in the regulations which activities can be Type D and which can be Type F.
Modules and other activities that can be entered independently in the booklet
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Introduction to smart contract programming for ethereum | D |
Sara Migliorini
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Python programming language | D |
Carlo Combi
(Coordinator)
|
| 1° 2° | Programming Challanges | D |
Romeo Rizzi
(Coordinator)
|
| years | Modules | TAF | Teacher |
|---|---|---|---|
| 1° 2° | Cooperative Game Theory in the (Deep) RL Era | D |
Alessandro Farinelli
(Coordinator)
|
Career prospects
Module/Programme news
News for students
There you will find information, resources and services useful during your time at the University (Student’s exam record, your study plan on ESSE3, Distance Learning courses, university email account, office forms, administrative procedures, etc.). You can log into MyUnivr with your GIA login details: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and also via the Univr app.
Career management
Attendance modes and venues
As stated in the Teaching Regulations, attendance at the course of study is not mandatory.
Part-time enrolment is permitted. Find out more on the Part-time enrolment possibilities page.
The course's teaching activities take place in the Science and Engineering area, which consists of the buildings of Ca‘ Vignal 1, Ca’ Vignal 2, Ca' Vignal 3 and Piramide, located in the Borgo Roma campus.
Lectures are held in the classrooms of Ca‘ Vignal 1, Ca’ Vignal 2 and Ca' Vignal 3, while practical exercises take place in the teaching laboratories dedicated to the various activities.
Graduation
Deadlines and administrative fulfilments
For deadlines, administrative fulfilments and notices on graduation sessions, please refer to the Graduation Sessions - Science and Engineering service.
Need to activate a thesis internship
For thesis-related internships, it is not always necessary to activate an internship through the Internship Office. For further information, please consult the dedicated document, which can be found in the 'Documents' section of the Internships and work orientation - Science e Engineering service.
Final examination regulations
The teaching activities related to the preparation of the final exam for the achievement of the degree and its verification consist of the preparation and discussion of a written paper in English (dissertation) related to the in-depth study of a scientific theme addressed in the course of studies, i.e. related to the analysis and solution of a case study (theoretical and/or directly derived from a problem of an industrial nature) or related to a work of an experimental type, which can also be developed within an internship course carried out at research institutions, schools, laboratories, and companies, or by taking advantage of study stays in Italy and abroad, or the result of autonomous and original research work, with related aspects of mathematical formalization, computer design, business-oriented realization. These activities may be carried out under the guidance of a supervisor at a university facility, or even outside the University of Verona, both in Italy and abroad, as long as it is recognized and accepted for this purpose in accordance with the Didactic Regulations of the Master's Degree Course in Artificial Intelligence. The CFUs assigned to the final examination (evaluation of the thesis) are 18. The committee in charge of the evaluation of the final exam (dissertation in English) is called to express an assessment that takes into account the entire course of study, carefully evaluating the degree of coherence between educational and professional objectives, as well as the candidate's capacity for autonomous intellectual elaboration, critical sense, communication skills, and general cultural maturity, in relation to the objectives of the Master's Degree course in Artificial Intelligence, and particular, in relation to the themes characterizing the dissertation.
Students may take the final examination only after they have fulfilled all other educational obligations set forth in their study plan and fulfillments at the administrative offices in accordance with the deadlines indicated in the general study manifesto.
The final evaluation and proclamation will be made by the final exam committee appointed by the chairperson of the teaching committee and composed of a chairperson and at least four other commissioners chosen from the faculty of the University.
The material submitted for the final examination is evaluated by the Thesis Evaluation Committee, composed of three faculty members, including possibly the thesis advisor, and appointed by the chair of the teaching college. The Thesis Evaluation Committee formulates an evaluation of the work done and forwards it to the final examination committee, which will make the final judgment.
The teaching committee shall regulate the procedures of thesis evaluation committees, final examination committees, and the scoring of the final examination by special regulations passed by the teaching committee.
