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 |
|---|
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
1 course among the following2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud, Visual intelligence, Statistical learning - 1st and 2nd year: Computer Vision & Deep Learning)2 modules among the following (1st year: Knowledge representation, Natural language processing, HCI Intelligent interfaces - 2nd year: AI & Cloud - 1st and 2nd year: Computer Vision & Deep learning)2 courses among the following (A.A. 2023/24: Complex systems and Network Science not activated)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.
Computer Vision & Deep Learning (2023/2024)
Teaching code
4S010679
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 2 dal Mar 4, 2024 al Jun 14, 2024.
Courses Single
Authorized
Learning objectives
Computer vision (CV) issues are traditionally tackled by adopting machine learning methods. Recently, deep learning (DL) approaches showed to face CV applications (e.g., detection, classification, segmentation, tracking, etc.) in a most effective and efficient way, reaching performance never imagined before, even allowing to tackle new problems (e.g., image generation, style transfer, to cite a few). This course aims at describing how main CV topics are faced, and even solved, by DL approaches: It will address classical, yet significant and with broad applicability in real scenarios, CV topics, which are stil open issues, involving image and video analysis and recognition, as well as other multimodal data (3D, audio, etc.). The most significant and effective DL approaches will be detailed including, but not limited to, convolutional neural networks, autoencoder architectures, recurrent models, domain adaptation frameworks, while addressing practical problems usually met in real applications such as scarcity of annotated data (unsupervised, self-supervised, few/zero-shot learning), data augmentation and generation, robustness to adversarial attacks, and continual lifelong learning.
Examination methods
To pass the exam, students must demonstrate:
- to have understood the theoretical principles and algorithms underlying the Computer Vision & Deep Learning techniques described in class;
- to be able to present their arguments in a precise, organic and structured way, without digressions;
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.
The exam will consist in the development of a project, followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked on the course contents described in class.
Prerequisites and basic notions
The prerequisites to follow the course consist in having acquired Machine Learning, Pattern Recognition and Artificial Intelligence skills in general.
Knowledge of topics related to image processing is also a fundamental part for understanding the course in question.
Program
The course intends to explain the modern methods for facing and solving Computational Vision problems. These methods basically consist in Deep Learning approaches applied to image and video processing.
In particular, the course will develop on monolithic topics related to specific open problems in Vision such as:
- Segmentation
- Object detection
- Object Recognition
- Image Classification
- Pose estimation
- Tracking
- Action & Activity Recognition
- Action localization
- 2D and 3D data reconstruction
- Image generation
- Image retrieval
- Multimodal data analysis
- Vision and Language
- Domain adaptation and generalization
- Transfer Learning and representation learning
- Training with scarce, noisy, unbalanced data
Didactic methods
The Theory lessons will take place in the classroom with slide projection, while the laboratory lessons will be carried out on the computer in the computer room. The latter will consist in the development of some of the algorithms explained during the Theory lectures.
Learning assessment procedures
To pass the exam, students must demonstrate:
- to have understood the theoretical principles and algorithms underlying the Computer Vision & Deep Learning techniques described in class;
- to be able to present their arguments in a precise, organic and structured way, without digressions;
- to know how to apply the acquired knowledge to solve application problems presented in the form of exercises, questions or projects.
The exam will consist in the development of a project, followed by the writing of a technical report and an oral presentation.
During the oral presentation of the project, questions will be asked on the course contents described in class.
Exam language
Inglese
