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 Computer Engineering for intelligent Systems - 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
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2° Year activated in the A.Y. 2025/2026
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3 modules among the following| Modules | Credits | TAF | SSD |
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3 modules among the following| Modules | Credits | TAF | SSD |
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4 modules among the following:
- 1st year: Advanced visual computing and 3d modeling, Computer vision, Embedded & IoT systems design, Embedded operating systems, Robotics
- 2nd year: Advanced control systemsLegend | 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.
Neurohealth (2025/2026)
Teaching code
4S012367
Credits
6
Coordinator
Language
English
Courses Single
Authorized
The teaching is organized as follows:
INTELLINGENT NEURO-DATA MODELING
Credits
5
Period
2nd semester
Academic staff
Ilaria Boscolo Galazzo
Lorenza Brusini
Francesca Benedetta Pizzini
Learning objectives
The course is composed by two modules: the first aims at covering practical aspects of the most common diagnostic tools and decision support systems in the neuroscience/neurological fields with a view toward their use in clinical settings. This offers the students the possibility to assess various applications of neuroimaging in clinical research, emphasizing the translational potentialities of bioengineering. Moreover, at the end of the course students will be : i) familiar with the functional and anatomical properties of the different brain areas and possible pathological changes; ii) able to critically interpret different brain images; iii) able to identify those image types and protocols that are more relevant for a given clinical scenario, for example for differential diagnosis in common neurological disorders or monitoring of therapeutic treatments. The second, Intelligent neuro-data modeling, aims at providing students with knowledge of proper engineering technologies and methodologies in the neuroengineering field (with focus on neuroimaging) through the analysis of the whole pipeline from problem definition to data acquisition and preprocessing, data analysis and decision making. At the end of the course the students will have deepened the fundamental principles of the most common neuroimaging sequences currently used. Moreover, they will be able to process and analyze different modalities with a critical view of the main steps in the pipeline, including data pre-processing, mathematical modelling and post-hoc analyses, also relying on artificial intelligence-based methods. They will be able to solve inherently interdisciplinary problems including both medical and information engineering aspects. The theory classes will be completed with practical laboratory sessions (MATLAB, Python and most common software for neuroimaging data processing), where examples with real data will be addressed in order to translate the learned methodologies into practical applications and solve real-world neuroimaging problems.
