OVERVIEW

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. 

AIMS AND CONTENT

LEARNING OUTCOMES

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.

PREREQUISITES

A firm grasp of the machine learning basics

RECOMMENDED READING/BIBLIOGRAPHY

Predictive Analytics (Eric Siegel, Wiley 2016)

TEACHERS AND EXAM BOARD

Exam Board

ALESSANDRO VERRI (President)

GIOVANNA GUERRINI

BARBARA CATANIA (President Substitute)

EXAMS

EXAM DESCRIPTION

Project discussion

Exam schedule