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
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 Artificial Intelligence - Enrollment from 2025/2026The 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
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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1 module among the following2 modules among the following2 modules among the following2 modules among the followingLegend | 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.
Visual Intelligence (2023/2024)
Teaching code
4S010686
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
ING-INF/06 - ELECTRONIC AND INFORMATICS BIOENGINEERING
Period
Semester 1 dal Oct 2, 2023 al Jan 26, 2024.
Courses Single
Authorized
Learning objectives
The course aims at providing competence about analysis, modeling and interpretation of multidimensional signals and images with focus on artificial vision and machine learning aspects, targeting applications in the field of multimedia and interpretable machine learning. At the end of the course the students will be able to autonomously solve typical problems requiring multidimensional signal modeling, feature extraction, analysis and interpretation of the outcomes of machine learning algorithms in the field of multimedia and artificial vision.
Examination methods
To pass the exam, students must demonstrate:
- to have understood the principles underlying visual intelligence
- to be able to present arguments on the topics of the course in a precise and organic way without digressions
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions and projects.
Prerequisites and basic notions
Fundamentals of signal and image processing
Program
The course aims at providing competence about analysis, modeling and interpretation of multidimensional signals and images with focus on artificial vision and machine learning aspects, targeting applications in the field of multimedia and interpretable machine learning. At the end of the course the students will be able to autonomously solve typical problems requiring multidimensional signal modeling, feature extraction, analysis and interpretation of the outcomes of machine learning algorithms in the field of multimedia and artificial vision with particular focus on convolutional neural networks.
Syllabus
The course consists of three blocks: modeling of the Human Visual System (HVS), multiresolution signal representation and analysis of deep learning algorithms with focus Convolutional Neural Networks (CNNs).
Part 1: Human Visual System (HVS) – 10 hours
Introduction to Visual Intelligence
Foudations of vision, stimulus encoding, representation and interpretation
HVS modeling: multiscale processing of the visual stimuli, Contrast Sensitivity Function (CSF), color vision and perception, Color Matching Functions (CMFs)
High-level modeling of the HVS: structural and functional connectivity and graph-based modeling
Part 2: Multiresolution analysis – 20 hours
Background
Mathematical tools
Fourier transform in 1D and 2D
Windowed Fourier Transform
Wavelets and multiresolution representations
Wavelets Bases
Families of Wavelet Transforms (WT) and their properties
Fast Discrete Wavelet Tranforms (DWT)
WT in two dimensions
Scattering transform
Part 3: Application to the analysis and interpretation of deep convolutional neural networks (CNNs)– 10 hours
Overview on CNNs
The issue of interpretability, main approaches
CNN, HVS and multiresolution: getting to a unified view
CNN interpretation based on multiresolution theory and HVS models
Examples of interpretable DL
LABORATORY
Laboratory sessions will consist in Matlab and Python exercises on the topics covered in the theory lessons.
Bibliography
Didactic methods
The lessons will be face to face. The recordings of previous years can be made available under request.
Learning assessment procedures
The exam will consist of a project and an interview on the topics covered in the course.
Evaluation criteria
To pass the exam, the student must demonstrate: - Understanding the fundamental theoretical aspects relating to the three parts of the course - Understanding the relationships between the topics covered and the differences and similarities at the modeling level - Having acquired skills theoretical and practical knowledge of multiresolution theory and its implications in the field of interpretability of deep machine learning models - Be able to transpose the skills acquired into solutions to concrete problems in a multidisciplinary environment.
Criteria for the composition of the final grade
The final grade will be determined by the outcome of the presentation of the project and the discussion
Exam language
Inglese
