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
The 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 It will be activated in the A.Y. 2026/2027
| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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| Modules | Credits | TAF | SSD |
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2 modules among:
- 1st year - Knowledge representation, Natural Language Processing, HCI - Multimodal Systems - delivered in 2025/2026
- 2nd year - AI & cloud - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer vision & deep learning - delivered in 2025/2026 and in 2026/2027
2 courses among (mutually exclusive with the previous ones):
- 1st year - Knowledge representation, Natural language processing, HCI - multimodal systems - delivered in 2025/2026
- 2nd year - AI & cloud, Visual intelligence - delivered in 2026/2027
- 1st and 2nd year - Advanced programming for AI, Computer Vision & deep learning, Statistical learning - delivered in 2025/2026 and in 2026/2027 2 courses among the following
- A.A. 2025/2026 Network Science not activated
- A.A. 2026/2027: Complex Systems not activated1 course 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.
Computational epistemology and philosophy (2025/2026)
Teaching code
4S010698
Teacher
Coordinator
Credits
6
Also offered in courses:
- Computational epistemology and philosophy of the course Master's degree in Artificial intelligence
Language
English
Scientific Disciplinary Sector (SSD)
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE
Period
1st semester dal Oct 1, 2025 al Jan 30, 2026.
Courses Single
Authorized
Learning objectives
Epistemology and Philosophy of Science The course is an introduction to the Contemporary Philosophy of Science, with the aim of highlighting the most meaningful moments in the development of the Science and the philosophical issues which are involved in. Expected outcomes: i) to be able to critically examine the fundamental assumptions of Positivism, the relationship between the twentieth century “linguistic turn” of Philosophy and the Philosophy of Science and, finally, the implications of the sociological methodology when applied to epistemological subjects, in order ii) to understand the conceptual grounds of the Philosophy of Science (i.e., realism, objectivity, experimental evidence, limits of validity, etc.); iii) to master the Epistemological lexicon; iv) to actively participate in the debates concerning developments (even the most recent ones) in the field of the Philosophy of Science.
Examination methods
To pass the exam, students must demonstrate:
- to have understood the principles underlying the philosophy of science and computational thinking
- to be able to present their arguments in a precise and organic way on the topics of the course, 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
The course does not require preliminary philosophical knowledge. The relevant philosophical concepts and theories will be explained as the course goes on.
Program
The Computational Philosophy and Philosophy of Science course aims to delve into the importance of the computational turn for the philosophy of science and the impact of artificial intelligence on society.
The course program is divided into two parts.
The first part deals with the concept of computation and explores the following topics:
- The computational turn in the philosophy of science
- The problem of computation and the logical conception of programming
- The impact of machine learning and big data
The second part discusses the consequences of artificial intelligence on scientific practice, society, and politics. The lectures will cover the following topics:
- The problem of bias and stereotypes
- Surveillance and autonomy
- Explainability and fairness.
Didactic methods
The course will be conducted using two main modalities: (1) traditional lectures and (2) seminar presentations. The lectures will be held in person and will not be streamed or recorded. Dedicated videos will be made available for: (1) explaining the examination procedures and grading, and (2) delving into specific parts of the course and important concepts. Although these videos are primarily intended as study support for non-attending students, attending students can also make use of them.
Learning assessment procedures
The final evaluation aims to determine not only the possession of knowledge but also the candidates' ability to argue correctly, to appropriately use the concepts and tools of the philosophy of science, and their creativity. The grade is obtained based on active participation in the seminar part, a paper (2000-5000 words) on a relevant topic related to the course, to be agreed upon with the instructor, and an oral examination according to the following proportions:
* 60% Oral examination
* 40% In-class presentation
The in-class presentation on a topic to be agreed upon with the instructor can also be done in a small group (maximum 3 people).
Evaluation criteria
The main assessment criteria are: (1) conceptual competence and (2) linguistic competence. Students will also find an evalutation rubric to help them compose the assignments.
Criteria for the composition of the final grade
* 60% Oral examination
* 40% In-class presentation
Exam language
English
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