Dettagli sull'Insegnamento per l'A.A. 2018/2019

Le informazioni che state leggendo si riferiscono all'anno accademico 2018/2019. Per questo molti collegamenti non sono disponibili e i dati potrebbero essere incompleti. Per leggere le informazioni correnti sul corso, se ancora erogato, consulta il catalogo corsi di ateneo. Per consultare le informazioni relative ad altri anni accademici, usa la lista in fondo a questa pagina.

A. Accademico:

2018/2019

Nome:

Knowledge, Language and Representation / Knowledge, Language and Representation

Tipo:

Corso singolo

Informazioni

Codice:

DT0346

SSD:

M-FIL/02

Crediti:

: Master Degree in Applied Data Science 6 CFU (b)

Periodo:

1^st semester

Erogazione:

Master Degree in Applied Data Science 1^st anno curriculum Data for Smart City Compulsory
Master Degree in Applied Data Science 1^st anno curriculum Data for Life Science Compulsory

Lingua:

Inglese

Docenti:

Giorgio Lando

Prerequisiti

No previous acquaintance with philosophy is required. A basic competence in propositional and predicate logic is useful, but not required.

Obiettivi

The course aims to make the students aware of the ways in which various kinds of knowledge, evidence, datum, theory, justification and induction are interpreted by contemporary epistemology. Moreover, it aims to illustrate and critically discuss how the availability of large data amounts might change the roles of data and theory in scientific research.

Sillabo

The definition of knowledge and Gettier’s thought experiments.
The value of knowledge; the roles of justification and rationality in knowledge; virtue epistemology.
Perception; sense data; a priori/a posteriori distinction; induction, the old and new riddles of induction.
Passive knowledge and understanding; Google-knowing; data, information and noise; information cascades.
The alleged end of theory; does the “data deluge” make scientific method obsolete?
Data-centric biology as an epistemological case study; data, experiments and theories in data-centric biology.

Descrittori di Dublino

Alla fine del corso, lo studente dovrebbe

know the fundamentals of epistemology; understand why epistemological concerns are important for data scientists and data analysts; apply this knowledge and understanding to specific case studies concerning data-driven activities and disciplines.

Testi di riferimento

Duncan Pritchard, What Is This Thing Called Knowledge? 4th Edition , Routledge. 2018. chs 1-10, 14
Michael Lynch, The Internet of Us. Knowing More and Understanding Less , Liveright. 2016.
Sabina Leonelli, Data-Centric Biology. A Philosophical Study , University of Chicago Press. 2016.

Modalità d'esame

The students will be assessed on the basis of a short written essay and of an oral exam. The short essay (3000-4000 words) should concern one of the topics in the reference texts (see above). The specific topic of the essay will be determined on the basis of the student’s interests and needs, during office hours. The essay should be submitted to the teacher by email, at least fourteen days before the oral exam. The essay is expected to show that the student masters the basic terminology of contemporary epistemology and is able to argue for a thesis in a logically structured and sound way. The oral exam consists of two open questions and concerns the topics discussed during the lectures.

Aggiornamenti alla pagina del corso

Le informazioni sulle editioni passate di questo corso sono disponibili per i seguenti anni accademici:

Per leggere le informazioni correnti sul corso, se ancora erogato, consulta il catalogo corsi di ateneo.

Ultimo aggiornamento delle informazioni sul corso: 30 luglio 2018, 15:54