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 semestre | Oct 1, 2015 | Jan 29, 2016 |
II semestre | Mar 1, 2016 | Jun 10, 2016 |
Session | From | To |
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Sessione straordinaria Appelli d'esame | Feb 1, 2016 | Feb 29, 2016 |
Sessione estiva Appelli d'esame | Jun 13, 2016 | Jul 29, 2016 |
Sessione autunnale Appelli d'esame | Sep 1, 2016 | Sep 30, 2016 |
Session | From | To |
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Sess. autun. App. di Laurea LM18-32 | Oct 21, 2015 | Oct 21, 2015 |
Sess. invern. App. di Laurea LM18-32 | Mar 17, 2016 | Mar 17, 2016 |
Sess. estiva App. di Laurea LM18-32 | Jul 13, 2016 | Jul 13, 2016 |
Sess. autun 2016 App. di Laurea LM18-32 | Oct 19, 2016 | Oct 19, 2016 |
Sess. invern. 2017 App. di Laurea-LM18-32 | Mar 21, 2017 | Mar 21, 2017 |
Period | From | To |
---|---|---|
Festività dell'Immacolata Concezione | Dec 8, 2015 | Dec 8, 2015 |
Vacanze di Natale | Dec 23, 2015 | Jan 6, 2016 |
Vancanze di Pasqua | Mar 24, 2016 | Mar 29, 2016 |
Anniversario della Liberazione | Apr 25, 2016 | Apr 25, 2016 |
Festa del S. Patrono S. Zeno | May 21, 2016 | May 21, 2016 |
Festa della Repubblica | Jun 2, 2016 | Jun 2, 2016 |
Vacanze estive | Aug 8, 2016 | Aug 15, 2016 |
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 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 activated in the A.Y. 2016/2017
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 e documents
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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
Documents and news
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PIANO DIDATTICO LM-18 LM-32 (xlsx, it, 18 KB, 21/09/18)
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: only in this way will you be able to receive notification of all the notices from your teachers and your secretariat via email and soon also via the Univr app.
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
List of theses and work experience proposals
Erasmus+ and other experiences abroad
Attendance
As stated in the Teaching Regulations for the A.Y. 2022/2023, attendance at the course of study is not mandatory.
Please refer to the Crisis Unit's latest updates for the mode of teaching.