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

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017 A.A. 2015/2016 A.A. 2014/2015 A.A. 2013/2014 A.A. 2012/2013 A.A. 2011/2012 A.A. 2010/2011 A.A. 2009/2010

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Ingegneria e scienze informatiche - Enrollment from 2025/2026

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

2° Year   activated in the A.Y. 2016/2017

ModulesCreditsTAFSSD
6
B
INF/01
6
B
ING-INF/05
Altre attivita' formative (taf f)
4
F
-
24
E
-
ModulesCreditsTAFSSD
12
B
ING-INF/05
6
B
ING-INF/05
12
B
INF/01
12
B
ING-INF/05
6
C
INF/01
6
B
ING-INF/05
6
B
ING-INF/05
activated in the A.Y. 2016/2017
ModulesCreditsTAFSSD
6
B
INF/01
6
B
ING-INF/05
Altre attivita' formative (taf f)
4
F
-
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Due 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
SECS-P/10
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

Advanced recognition systems  (2016/2017)

Teaching code

4S02792

Teacher

Marco Cristani

Coordinator

Marco Cristani

Credits

6

Language

Italian

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

Period

I sem. dal Oct 3, 2016 al Jan 31, 2017.

Seminars 0

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.

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