| CODE | 101801 |
|---|---|
| ACADEMIC YEAR | 2021/2022 |
| CREDITS |
|
| SCIENTIFIC DISCIPLINARY SECTOR | INF/01 |
| LANGUAGE | English |
| TEACHING LOCATION |
|
| SEMESTER | 2° Semester |
| TEACHING MATERIALS | AULAWEB |
The course provides a hands-on approach to predictive analytics. In.a few interactive lectures we learn the basic steps of any predictive analytics project: 1 understanding the problem, 2 identifying the machine learning tools to be used, 3 reviewing pros and cons of the available techniques, 4. looking for an appropriate data set, 5. running the algos and commenting the obtained results. Each student is then encouraged to find a project of suitable size and work on it.
Learning the key elements of conceptual and notational tools for business modelling and the ability of approaching data mining as a process - including the business understanding, data understanding, exploratory data analysis, modeling, evaluation, and deployment phases -, and of employing a wide range of mining techniques for data analysis.
A firm grasp of the machine learning basics
Predictive Analytics (Eric Siegel, Wiley 2016)
Office hours: Appointment by email
ALESSANDRO VERRI (President)
GIOVANNA GUERRINI
BARBARA CATANIA (President Substitute)
Project discussion
| Date | Time | Location | Type | Notes |
|---|---|---|---|---|
| 26/01/2022 | 09:00 | GENOVA | Esame su appuntamento | Esame su appuntamento, contattare i docenti |
| 27/06/2022 | 09:00 | GENOVA | Esame su appuntamento | Esame su appuntamento, contattare i docenti |
| 14/09/2022 | 09:00 | GENOVA | Esame su appuntamento | Esame su appuntamento, contattare i docenti |
| 25/01/2023 | 09:00 | GENOVA | Esame su appuntamento | Esame su appuntamento, contattare i docenti |