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, 2013 | Jan 31, 2014 |
II semestre | Mar 3, 2014 | Jun 13, 2014 |
Session | From | To |
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Sessione straordinaria | Feb 3, 2014 | Feb 28, 2014 |
Sessione estiva | Jun 16, 2014 | Jul 31, 2014 |
Sessione autunnale | Sep 1, 2014 | Sep 30, 2014 |
Session | From | To |
---|---|---|
Sessione autunnale | Oct 17, 2013 | Oct 17, 2013 |
Sessione straordinaria | Dec 11, 2013 | Dec 11, 2013 |
Sessione invernale | Mar 20, 2014 | Mar 20, 2014 |
Sessione estiva | Jul 15, 2014 | Jul 15, 2014 |
Period | From | To |
---|---|---|
Vacanze Natalizie | Dec 22, 2013 | Jan 6, 2014 |
Vacanze di Pasqua | Apr 17, 2014 | Apr 22, 2014 |
Festa del S. Patrono S. Zeno | May 21, 2014 | May 21, 2014 |
Vacanze Estive | Aug 11, 2014 | Aug 15, 2014 |
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
Residori Stefania
stefania.residori@univr.itStudy 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
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2° Year activated in the A.Y. 2014/2015
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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.
Machine Learning & Pattern Recognition (2013/2014)
Teaching code
4S02803
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Period
I semestre dal Oct 1, 2013 al Jan 31, 2014.
Location
VERONA
Learning outcomes
Pattern Recognition is a highly pervasive discipline, both for science and industry. It focuses on the creation of classifiers, that is, algorithms able to learn aspects of the reality that surrounds us and to make appropriate decisions when in the presence of new stimuli. Speech recognition, automotive, surveillance systems, quality control systems, recommender systems, search engines, social networks, interactive tools (Kinect, Wii) are just some of the many applications that rely on the presence of classifiers. The Pattern Recognition course is intended to provide the methodological principles at the basis of the classification, together with the most modern techniques that can solve problems until a few years ago unmanageable. In other words, the course aims to be the best compromise between theory and practice, making the student can solve problems with tangible and important techniques from solid theoretical point of view.
Program
The course can be divided into two parts, the methodology and the application, which go hand in hand during the course.
Methodologies
- Introduction
- Recognition and classification
- Bayesian Decision Theory
- Parameters Estimation
- Nonparametric Methods of Parameters Estimation
- Linear and non-linear discriminant functions
- Extraction and feature selection, PCA, Fisher transform
- Expectation-Maximization Algorithm on mixtures of Gaussians
- Generative and discriminative methods
- Kernel Methods and Support Vector Machines
- Hidden Markov Models
- Methods for unsupervised classification (clustering)
- Pattern recognition for the analysis and recognition in images and videos
Applications
- Face recognition
- Tracking
- Video surveillance
Textbooks:
- Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.
Examination Methods
Project and oral.
Teaching materials e documents
- cap. 0 - Informazioni (vnd.ms-powerpoint, it, 369 KB, 02/10/13)
- cap. 1 - Inquadramento del corso e nozioni di base (vnd.ms-powerpoint, it, 5707 KB, 07/10/13)
- cap. 2 - Teoria della decisione di Bayes (vnd.ms-powerpoint, it, 1039 KB, 14/10/13)
- cap. 3BIS - Estrazione delle feature - FDA (pdf, it, 810 KB, 14/10/13)
- cap. 3 - Estrazione delle feature - PCA (vnd.ms-powerpoint, it, 1276 KB, 14/10/13)
- cap. 3TRIS - Estrazione delle feature - Bag of Words (vnd.ms-powerpoint, it, 2136 KB, 14/10/13)
- cap. 4 - Stima parametrica dei parametri (zip, it, 5944 KB, 25/11/13)
- cap. 5 - Stima non parametrica di modelli (zip, it, 3484 KB, 09/12/13)
- cap. 6 - Clustering (zip, it, 9420 KB, 13/01/14)
- lab. 0 - Ripasso MATLAB (zip, it, 0 KB, 07/10/13)
- lab. 10 - Non parametric estimation (zip, it, 3 KB, 18/12/13)
- lab. 11 - Clustering (zip, it, 6 KB, 15/01/14)
- lab. 1 - Classificatori di Bayes (zip, it, 6504 KB, 16/10/13)
- lab. 2 - Classificatori di Bayes (cont.) (zip, it, 6504 KB, 16/10/13)
- lab. 3 - Classificatori di Bayes (funzioni discriminanti) (zip, it, 1 KB, 30/10/13)
- lab. 4 - Principal Component Analysis (zip, it, 1 KB, 30/10/13)
- lab. 5;6 - PCA and eigenfaces (zip, it, 5045 KB, 11/11/13)
- lab. 7 - Fisher Discriminant Analysis (zip, it, 2 KB, 11/11/13)
- lab. 8 - fisherfaces (zip, it, 5043 KB, 20/11/13)
- lab. 9 - Expectation Maximization (zip, it, 5047 KB, 04/12/13)
- Progetti di fine corso (octet-stream, it, 4608 KB, 13/01/14)
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: 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.
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 thesis proposals
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.