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 |
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| 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.
Embedded AI (2023/2024)
Teaching code
4S010696
Credits
6
Coordinator
Language
English
Courses Single
Authorized
The teaching is organized as follows:
Learning objectives
The course aims at providing the following knowledge: techniques for the automatic design of embedded and industrial IoT systems, starting from their specifications to go through verification, automatic synthesis and testing. Main languages to deal with this kind of project and the most advanced automatic tools for their manipulation. This is in particular applied to the design, verification and test of cyber-physical production systems.
At the end of the course the student will have to demonstrate that he/she has the following skills to apply the acquired knowledge: identify the best architecture for an embedded and industrial IoT system from the specifications; model, design and verify complex analog / digital devices; develop embedded software and interact with IoT and cloud architectures; partition a functionality between hw, sw with attention to the network and operating systems; build project report highlighting the critical aspects resolved; be able to use additional languages for the design of embedded and industrial IoT systems starting from the ones studied in the course.
Examination methods
To pass the exam, the student has to demonstrate:
- he/she has understood the principles related to the parallel programming
- he/she is able to describe the concepts in a clear and exhaustive way without digressions
- he/she is able to apply the acquired knowledge to solve application scenarios described by means of exercises, questions and projects.
Prerequisites and basic notions
None
Criteria for the composition of the final grade
The final grade will be the average of the grades of the two parts (part I - Fummi, part II Bombieri). The grade for each part will be assigned through a partial test or part of the standard exam.
