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.

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.

CURRICULUM TIPO:

1° Year 

ModulesCreditsTAFSSD
12
B
ING-INF/05
12
B
INF/01
12
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05

2° Year   activated in the A.Y. 2015/2016

ModulesCreditsTAFSSD
6
B
ING-INF/05
6
B
INF/01
Altre attivita' formative (taf f)
4
F
-
24
E
-
ModulesCreditsTAFSSD
12
B
ING-INF/05
12
B
INF/01
12
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05
6
B
ING-INF/05
activated in the A.Y. 2015/2016
ModulesCreditsTAFSSD
6
B
ING-INF/05
6
B
INF/01
Altre attivita' formative (taf f)
4
F
-
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Tre insegnamenti a scelta
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
SECS-P/10
6
C
INF/01
6
C
INF/01
6
C
INF/01
6
C
FIS/01
Between the years: 1°- 2°
8
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Machine Learning & Pattern Recognition  (2014/2015)

Teaching code

4S02803

Teacher

Marco Cristani

Coordinator

Marco Cristani

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Period

I sem. dal Oct 1, 2014 al Jan 30, 2015.

Seminars 0

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.

Reference texts
Author Title Publishing house Year ISBN Notes
R. Duda, P. Hart, D. Stork Pattern Classification Wiley 2001
C.M. Bishop Pattern Recognition and Machine Learning Springer 2006

Examination Methods

Project and oral exam.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Teaching materials e documents