The Epistemology of Big Data
A.Y. 2024/2025
Learning objectives
The course aims at developing the logico-mathematical background to assess critically the logic and episte-mology of inductive reasoning, or "reasoning with data". In addition to making students familiar with the rel-evant elementary logical, probabilistic and statistical notions, it focusses on how the formalisation of induc-tive inference sheds crucial methodological light on the "datacentric" revolution, which is currently dotting the development of the natural and social sciences.
Expected learning outcomes
Students are expected to acquire a full understanding of the formal notions presented and master basic knowledge of the following topics:
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts in elementary probability theory
- know how to apply elementary logic to formalize probabilistic concepts
- understand the epistemological questions related to inductive reasoning
- understand the relevance of a proper the epistemology of inductive inference in the wider methodological discussion on "big data"
- know the central concepts and reasoning tools of discrete mathematics
- know the central concepts in elementary probability theory
- know how to apply elementary logic to formalize probabilistic concepts
- understand the epistemological questions related to inductive reasoning
- understand the relevance of a proper the epistemology of inductive inference in the wider methodological discussion on "big data"
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Educational website(s)
Professor(s)
Reception:
Friday 8:30-11:30
Second Floor, Cortile Ghiacchiaia. Please email me to secure your slot.