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.

1° Year

ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05

2° Year  activated in the A.Y. 2022/2023

ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
ModulesCreditsTAFSSD
9
B
ING-INF/04
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
6
B/C
INF/01
6
B/C
ING-INF/05
Compulsory activities for Smart Systems & Data Analytics
6
B/C
INF/01 ,ING-INF/06
6
B/C
ING-INF/05
activated in the A.Y. 2022/2023
ModulesCreditsTAFSSD
Compulsory activities for Embedded & Iot Systems
Compulsory activities for Robotics Systems
Compulsory activities for Smart Systems & Data Analytics
6
B/C
ING-INF/05
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Between the years: 1°- 2°
Further activities
3
F
-
Between the years: 1°- 2°
Training
3
F
-

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.




S Placements in companies, public or private institutions and professional associations

Teaching code

4S009018

Credits

6

Coordinator

Marco Cristani

Language

English en

Scientific Disciplinary Sector (SSD)

INF/01 - INFORMATICS

The teaching is organized as follows:

Teoria

Credits

5

Period

Primo semestre

Laboratorio

Credits

1

Period

Primo semestre

Academic staff

Marco Cristani

Learning outcomes

The course provides the theoretical foundations and describes advanced methodologies related to the area of deep learning. Deep learning solves machine learning and pattern recognition problems through the neural network paradigm and the numerical optimization. The course is also highly implementative, offering specific programming concepts for professional Python-based systems. Deep learning aims to build mainly nonlinear regression and classification systems based on neural networks. A neural network can be seen as a simple computational structure (multinomial logistic functions + non-linearities, intertwined together) which is enhanced by structuring itself at various levels (layers) of various types (fully connected, convolutional, recurring, and many others). Each of these structures underlies a very precise theory (for example the dropout of neural networks refers to the Bayesian approximation) which will be formally detailed by the teacher. In this way, the student will not only be a user of the discipline, but will manage it by acquiring formal critical skills. Particular attention will also be paid to the aspect of explainability, that is, all those techniques capable of communicating critical cases in which a particular neural network is unable to solve problems. The course will provide case studies on which to apply the studied techniques, to make them immediately usable in a professional context.

Program

The course presents a series of state-of-the-art topics in the field of recognition. Deep learning will be analyzed starting from its methodological basis. 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:
- Linear Regression, ridge, LASSO, elastic net
- Multinomial Logistic Classifier,
- Neural Networks,
- Backpropagation,
- Convolutional Neural Network,
- Recurrent Neural Networks
- Long Short-Term Memory machine
- Transformer 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