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 |
---|---|---|
I semestre | Oct 1, 2020 | Jan 29, 2021 |
II semestre | Mar 1, 2021 | Jun 11, 2021 |
Session | From | To |
---|---|---|
Sessione invernale d'esame | Feb 1, 2021 | Feb 26, 2021 |
Sessione estiva d'esame | Jun 14, 2021 | Jul 30, 2021 |
Sessione autunnale d'esame | Sep 1, 2021 | Sep 30, 2021 |
Session | From | To |
---|---|---|
Sessione Estiva | Jul 19, 2021 | Jul 19, 2021 |
Sessione Autunnale | Oct 19, 2021 | Oct 19, 2021 |
Sessione Autunnale Dicembre | Dec 7, 2021 | Dec 7, 2021 |
Sessione Invernale | Mar 17, 2022 | Mar 17, 2022 |
Period | From | To |
---|---|---|
Festa dell'Immacolata | Dec 8, 2020 | Dec 8, 2020 |
Vacanze Natalizie | Dec 24, 2020 | Jan 3, 2021 |
Epifania | Jan 6, 2021 | Jan 6, 2021 |
Vacanze Pasquali | Apr 2, 2021 | Apr 5, 2021 |
Festa del Santo Patrono | May 21, 2021 | May 21, 2021 |
Festa della Repubblica | Jun 2, 2021 | Jun 2, 2021 |
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.
Academic staff
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 |
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2° Year activated in the A.Y. 2021/2022
Modules | Credits | TAF | SSD |
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1 module among the following
3° Year activated in the A.Y. 2022/2023
Modules | Credits | TAF | SSD |
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1 module among the following
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1 module among the following
Modules | Credits | TAF | SSD |
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1 module among the following
Modules | Credits | TAF | SSD |
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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.
Type D and Type F activities
Le attività formative in ambito D o F comprendono gli insegnamenti impartiti presso l'Università di Verona o periodi di stage/tirocinio professionale.
Nella scelta delle attività di tipo D, gli studenti dovranno tener presente che in sede di approvazione si terrà conto della coerenza delle loro scelte con il progetto formativo del loro piano di studio e dell'adeguatezza delle motivazioni eventualmente fornite.
years | Modules | TAF | Teacher |
---|---|---|---|
3° | Matlab-Simulink programming | D |
Bogdan Mihai Maris
(Coordinator)
|
years | Modules | TAF | Teacher |
---|---|---|---|
3° | Introduction to 3D printing | D |
Franco Fummi
(Coordinator)
|
3° | Python programming language | D |
Vittoria Cozza
(Coordinator)
|
3° | HW components design on FPGA | D |
Franco Fummi
(Coordinator)
|
3° | Rapid prototyping on Arduino | D |
Franco Fummi
(Coordinator)
|
3° | Protection of intangible assets (SW and invention)between industrial law and copyright | D |
Roberto Giacobazzi
(Coordinator)
|
years | Modules | TAF | Teacher | |
---|---|---|---|---|
1° | Subject requirements: mathematics | D |
Rossana Capuani
|
|
3° | The fashion lab (1 ECTS) | D |
Maria Caterina Baruffi
(Coordinator)
|
|
3° | LaTeX Language | D |
Enrico Gregorio
(Coordinator)
|
Pattern recognition and Signal and image Processing in Bioinformatics (2022/2023)
The teaching is organized as follows:
Signal and image processing I
Credits
6
Period
See the unit page
Academic staff
See the unit page
Information recognition and retrieval for bioinformatics
Credits
6
Period
See the unit page
Academic staff
See the unit page
Learning objectives
The aim of the course is to provide the theoretical and practical foundations of data processing and modeling in the field of bioinformatics, with particular emphasis on signal and image processing and pattern recognition. The course comprises two modules as detailed below. Module1 (Pattern Recognition): This module is aimed at providing the theoretical and applicative bases of Pattern Recognition, a class of automatic methodo-logies used to recognize and recover information from biological data. In particular, during the course the main techniques of this area will be presented and discussed, in particular linked to representa-tion, classification, clustering and validation. The focus is more on the description of the employed methodologies rather than on the details of applicative programs (already seen in other courses). Af-ter attending the course, the students will be able to analyse a biological problem from a Pattern Recognition perspective; they will also have the skills needed to invent, develop and implement the dif-ferent components of a Pattern Recognition System. Module2 (Signal and image processing 1): The course aims at providing the students with the fundamentals of signal and image processing with the emphasis on aspects that are relevant in the field of bioinformatics at both theoretical and practical levels. At the end of the course the students will be able to analyze the typical signal and image processing problems encountered in bioinformatics as well as to devise and implement suitable solutions grounding on the knowledge gained in the theory sessions and using the main available toolboxes.
Prerequisites and basic notions
Foundations of functional analysis.
Basic knowledge of probability and mathematics
Bibliography
Criteria for the composition of the final grade
To pass the exam, the student must prove:
- the ability to clearly and concisely describe the different components of a Pattern Recognition System
- the ability to analise, understand and describe a Pattern Recognition system (or a given part of it) relative to a biological problem
- the ability to analyze the typical signal and image processing problems encountered in bioinformatics as well as to devise and implement suitable solutions grounding on the knowledge gained in the theory sessions and using the main available toolboxes;
- the ability to describe the concepts in a clear and exhaustive way;
- the ability to apply the acquired knowledge to solve application scenarios described by means of questions and exercises.
The exam of INFORMATION RETRIEVAL AND SIGNAL AND IMAGE PROCESSING FOR BIOINFORMATICS consists of two parts:
i) a written exam of INFORMATION RETRIEVAL containing questions on topics presented during the course (30 points available). The (i) written part is passed if the grade is greater or equal to 18;
ii) a written exam of SIGNAL AND IMAGE PROCESSING consisting in questions concerning the topics of both the theory and the laboratory sessions, with respective weight of 1/2 and one exercise (15 punti). In case the exam would be in teleconference mode the exam would be be in oral form.
The two parts of the exam can be passed separately: the final grade is the mean of the two grades.
The total exam is passed if each of the two grades is greater or equal than 18. Each evaluation is maintained valid for the whole academic year.
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.
Tutoring faculty members
Graduation
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.