1. [I]nfo
  2. [M]oduli
  3. [B]acheca
  4. [P]rogramma
  5. [O]rari
  6. [E]sami
  7. E[v]enti
  8. [F]iles

Generali:

  • Dipartimento: Ingegneria
  • Settore Ministeriale: ING-INF/03
  • Codice di verbalizzazione: 80300130
  • Metodi di insegnamento: Frontale E Altro
  • Metodi di valutazione: Scritto E Orale
  • Prerequisiti: Probability Theory (or equivalent).
  • Obiettivi: Information Theory, as initiated by Claude Shannon in 1948, is the science that allows to mathematically quantify the information content of a message and its transfer through a system. In particular, information theory provides mathematical tools to deal with a quantitative view of information content and with a qualitative view of information transfer. The main contribution of Shannon was to establish bounds on communication. However, a general definition of Information is: new knowledge derived from study, experience, messages, etc. Therefore, Information is linked to Knowledge. Companies today are rich in terms of data but poor in terms of useful information extracted from data. Data science techniques can help companies discover knowledge and acquire business intelligence from their massive datasets. This course will give a unified view of Information, Knowledge and Data Analysis, extending the applications of Information Theory beyond the classical theory of communications. In particular, the course focuses on statistical methods for data analysis. Applications of information theory and data science in several areas will be presented including: digital communications, economics, marketing, biology, medicine, meteorology.
  • Ricevimento: Su appuntamento tramite richiesta per email.

Didattica:

  • A.A.: 2023/2024
  • Canale: UNICO
  • Crediti: 9
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