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 in Informatica - 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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Mathematical analysis 1
Computer Architecture
2° Year activated in the A.Y. 2023/2024
Modules | Credits | TAF | SSD |
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3° Year activated in the A.Y. 2024/2025
Modules | Credits | TAF | SSD |
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1 module among the following
Modules | Credits | TAF | SSD |
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Mathematical analysis 1
Computer Architecture
Modules | Credits | TAF | SSD |
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Modules | Credits | TAF | SSD |
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1 module among the following
Modules | Credits | TAF | SSD |
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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.
Artificial Intelligence (2024/2025)
Teaching code
4S00075
Teacher
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Prerequisites and basic notions
Programming, algorithms, logic, probability, at the level offered by the bachelor degree in Computer Science.
Program
i) Problem solving: solving problems by searching; search in complex environments; constraint satisfaction problems;
ii) Knowledge, reasoning and planning: logic-based agents; knowledge representation; automated planning;
iii) Uncertain knowledge and reasoning: quantifying uncertainty; probabilistic reasoning; sequential decision making;
iv) Machine Learning: learning from examples; reinforcement learning.
Bibliography
Didactic methods
Classroom lessons and laboratory lessons for assisted code production.
Learning assessment procedures
The exam consists of a project that includes a significant programming part or of a written exame, both modalities will focus on the solution techniques studied during the course
Evaluation criteria
To pass the exam, students must demonstrate that they:
- have understood the principles underlying the techniques studied during the course;
- know how to apply the knowledge acquired to solve problems presented in the form of projects.
Criteria for the composition of the final grade
The final grade will be based on the evaluation of the written exam or on the evaluation of the project, the evaluation of the project includes an oral exam in which the project is presented to the teacher.
Exam language
Italiano.