96586 - BIG DATA

Course contents

The protagonists of this lab are NOT numbers or statistics, but people.


The approach used will be predominantly humanistic. How does BIG DATA enable decision-making processes?


The 3 main types of algorithmic learning used in machine learning will be studied:


supervised, unsupervised and reinforcement.


Each of these respectively answers these three questions:


What impact do they have on predictions? (supervised learning)


How do they generate detailed clusters? (unsupervised learning)


Finally, how do they create alternative scenarios, following a criterion of IF...? THEN...? (reinforcement learning)


The lab therefore aims to make students aware of the impact that BIG DATA has and will have in the real world.

Readings/Bibliography

This is a lab, so attendance is strongly recommended as reading hints will be provided during the lecture, as the subject is so new that bibliographic references are in flux.


However, this text is recommended:


Augmented Marketing. A guide to new martech scenarios, by VIncenzo Cosenza, Apogeo

Teaching methods

Lectures, group work

Assessment methods

The lab includes a final project work written by small groups.


The chosen theme will be free. Each group will have to study the impact of BIG DATA on a certain topic and hypothesise which algorithms to use in the chosen field.

 

For those NOT attending: on the dates indicated as exam sessions, they will have to draw up an individual project taking into account the indications contained in the textbook. In this paper, maximum 4 pages, they will have to apply the 3 types of algorithms to possible case studies. On the day of the appeal they will have to send the essay.

Teaching tools

Supporting slides and research papers will be provided.

Office hours

See the website of Riccardo Pirazzoli