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 |
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I sem. | Oct 3, 2016 | Jan 31, 2017 |
II sem. | Mar 1, 2017 | Jun 9, 2017 |
Session | From | To |
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Sessione invernale Appelli d'esame | Feb 1, 2017 | Feb 28, 2017 |
Sessione estiva Appelli d'esame | Jun 12, 2017 | Jul 31, 2017 |
Sessione autunnale Appelli d'esame | Sep 1, 2017 | Sep 29, 2017 |
Session | From | To |
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Sessione estiva Appelli di Laurea | Jul 19, 2017 | Jul 19, 2017 |
Sessione autunnale Appelli di laurea | Oct 18, 2017 | Oct 18, 2017 |
Sessione invernale Appelli di laurea | Mar 21, 2018 | Mar 21, 2018 |
Period | From | To |
---|---|---|
Festa di Ognissanti | Nov 1, 2016 | Nov 1, 2016 |
Festa dell'Immacolata Concezione | Dec 8, 2016 | Dec 8, 2016 |
Vacanze di Natale | Dec 23, 2016 | Jan 8, 2017 |
Vacanze di Pasqua | Apr 14, 2017 | Apr 18, 2017 |
Anniversario della Liberazione | Apr 25, 2017 | Apr 25, 2017 |
Festa del Lavoro | May 1, 2017 | May 1, 2017 |
Festa della Repubblica | Jun 2, 2017 | Jun 2, 2017 |
Vacanze estive | Aug 8, 2017 | Aug 20, 2017 |
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 Enrolment FAQs
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 enrolment year.
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1° Year
Modules | Credits | TAF | SSD |
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2° Year
Modules | Credits | TAF | SSD |
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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.
Advanced recognition systems (2016/2017)
Teaching code
4S02792
Teacher
Coordinatore
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
I sem. dal Oct 3, 2016 al Jan 31, 2017.
Learning outcomes
The course is thought of as a natural continuation of Pattern Recognition, and it approaches considerably more difficult classification problems. The course objectives are to make the student able to understand and modify professional recognition code (OpenCV, VLFeat, Tensorflow), and understand the underlying theory. At the end of the course, the student will have to face a real recognition problem (derived from an industrial application), presenting the most proper solution. The languages used will be MATLAB and Python, with some references to C.
Program
The course presents a series of state-of-the-art topics in the field of recognition. Each topic will be explained through updated articles together with the lesson slides. The following books are suggested as a reference:
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT Press, 2016.
Topics:
- Classification validation tools: Confusion matrix and derivative measurements, ROC and CMC curves, average precision, average quadratic error, label correlation, grading and regression measures
- Kernel machines, Support Vector Machines
- VLFeat for object recognition: Dense object recognition through multiclass discriminatory models
- Dense classification features as bag of words
- Shape descriptors for object tracking: B-spline and Condensation
- Deep learning in Tensorflow: Multinomial Logistic Classifier, Neural Networks, Convolutional Neural Network
Examination Methods
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.
Teaching materials
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Lab 1 - Valutazione dei classificatori supervisionati (zip, it, 400 KB, 06/10/16)
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Lab 2 - Funzioni discriminanti lineari ed SVM (zip, it, 1925 KB, 20/10/16)
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Lab 3 - Riconoscimento di oggetti con BoW (zip, it, 14 KB, 17/11/16)
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Lab 4 - Riconoscimento di oggetti con PLSA (zip, it, 1 KB, 24/11/16)
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Lab 6 - Shape descriptors (zip, it, 1877 KB, 19/12/16)
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Lezione 0 - Introduzione al corso (pdf, it, 719 KB, 02/10/16)
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Lezione 1 - Valutazione dei classificatori supervisionati (zip, it, 2874 KB, 02/10/16)
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Lezione 2 - Classificatori discriminativi (zip, it, 5315 KB, 10/10/16)
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Lezione 4 - Riconoscimento di Oggetti - Classificatori generativi (PLSA) (zip, it, 17248 KB, 24/11/16)
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Lezione 5 - Motion Detection (zip, it, 7103 KB, 30/11/16)
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Lezione 7 - Deep Networks (zip, it, 8017 KB, 12/01/17)
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Lezione 8 - Deep Networks - Going Deep (zip, it, 1344 KB, 11/01/17)
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Lezione 9 - Deep Networks - CNNs (zip, it, 8047 KB, 23/01/17)
Type D and Type F activities
Modules not yet included
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.
Further services
I servizi e le attività di orientamento sono pensati per fornire alle future matricole gli strumenti e le informazioni che consentano loro di compiere una scelta consapevole del corso di studi universitario.
Graduation
List of theses and work experience proposals
Attendance
As stated in point 25 of the Teaching Regulations for the A.Y. 2021/2022, attendance at the course of study is not mandatory.Please refer to the Crisis Unit's latest updates for the mode of teaching.