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 activated in the A.Y. 2021/2022
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1 module among the following (1st year: Big Data epistemology and Social research; 2nd year: Cybercrime, Data protection in business organizations, Comparative and Transnational Law & Technology)2 courses among the following (1st year: Business analytics, Digital Marketing and market research; 2nd year: Logistics, Operations & Supply Chain, Digital transformation and IT change, Statistical methods for Business intelligence)2 courses among the following (1st year: Complex systems and social physics, Discrete Optimization and Decision Making, 2nd year: Statistical models for Data Science, Continuous Optimization for Data Science, Network science and econophysics, Marketing research for agrifood and natural resources)2 courses among the following (1st year: Data Visualisation, Data Security & Privacy, Statistical learning, Mining Massive Dataset, 2nd year: Machine Learning for Data Science)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.
Big data epistemology (2020/2021)
Teaching code
4S009088
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
M-FIL/02 - LOGIC AND PHILOSOPHY OF SCIENCE
Period
II semestre dal Mar 1, 2021 al Jun 11, 2021.
Learning outcomes
The course will allow the student to acquire the skills necessary to apply the key concepts of epistemology (knowledge, methodology, justification, explanation, etc.) to the specific case of data science and to the discussion of consequences and implications of big data for society in general.
At the end of the course the student has to show to have acquired the following skills:
● recognize and discuss the main epistemological issues relating to the knowledge produced by the collection and manipulation of big data, in particular for what concerns the topics: (1) epistemological specificity of big data; (2) the impact of big data on scientific work; (3) Big Data and cultural authority of science
● having acquired, through detailed analysis of real life situations, the tools for a more conscious and critical approach to the work of data analyst, as well as for the management and dissemination of big data in public domains.
Program
The course is dedicated to exploring the epistemological, social, and political issues related to big data, machine learning, and artificial intelligence. The program is divided into two main moduli:
(A) Producing knowledge in the digital age. This module will deal with the epistemological questions raised by the use of machine learning and big data in the production of scientific knowledge. Examples of such questions are: How do big data change scientific practices and methods? What are the limits of the computational approach to science? Do big data make theories superfluous? What are the epistemological features of statistical learning? The structure of the module is as follows:
(A.1) Introduction to the epistemology of computability: complexity and undecidability.
(A.2) The concept of data and the computational theory of the mind.
(A.3) Machine Learning, big data, and the scientific method.
(B) The social epistemology of big data. The second module is concerned with the socio-epistemological and political impact of machine learning and big data on scientific practice and society at large. Examples of questions tackled in this module are: How does the social structure of scientific research change as a result of using big data? How can one make artificial intelligence more explainable and accountable? How does machine learning affect digital environments such as social networks? The structure of the module is as follows:
(B.1) Scientific research and Big Data.
(B.2) Explainable Artificial Intelligence
(B.3.) Truth and post-truth in digital environments.
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Donald Gillies | Artificial Intelligence and Scientific Method | Oxford University Press | 1996 | ||
| Marcello Frixione e Dario Palladino | Funzioni, macchine, algoritmi | Carocci | 2004 | ||
| Sabina Leonelli | La ricerca scientifica nell'era dei big data | Meltemi | 2018 | ||
| Nils Nilsson | The Quest for Artificial Intelligence: A History of Ideas and Achievements | Cambridge University Press | 2009 | ||
| Francesco Berto | Tutti pazzi per Gödel | Laterza | 2008 |
Examination Methods
The course will combine introductory lectures and class discussions in the form of reading seminars. The final assessment is the result of three elements:
(1) A class presentation of a text or an issue (30%)
(2) A written assignment (max 3000 words) (30%)
(3) Oral exam (40%)
