OVERVIEW

These lectures provide the students with the comprehension of the main conceptual and computational tools concerned with the interpretation of big amount of data with predictive purposes. Specifically, as far as the data analysis is concerned, the lectures will describe the crucial aspects related to the processing of time series, introduce the Bayesian analysis and filtering, and provide the basics of pattern recognition. The second part of the lectures will be devoted to discuss the main predictive approaches, including regularization theory, machine and deep learning. The teaching approach will combine theoretical aspectes with focus on applications in economics and other applied sciences

AIMS AND CONTENT

LEARNING OUTCOMES

The aim of these lectures is to provide students with a fair understanding of the main conceptual and computational tools concerned with the interpretation of big amount of data and with the use of such data for predictive purposes.

AIMS AND LEARNING OUTCOMES

The first part of the course involves the crucial aspected related to the processing of time series, the Bayesian analysis and filtering and the basics of pattern recognition. The second part will discuss the main predictive approaches, such as regularization theory, machine and deep learning. The teaching approach will combine the description of the main theoretical aspects of data analysis with some focus on applications in economics and applied sciences

At the end of the course the students will gain some insights in the computational data analysis world and in the comprehension of some aspects of artificial intelligence. Further, they will obtain skills concerning the computational tools for the processing of data and time series with predictive purposes

PREREQUISITES

R or Matlab programming

Data formats and I/O issues

Basic aspects of numerical analysis and statistics

TEACHING METHODS

Frontal teaching (24 h) and computer projects (24 h)

SYLLABUS/CONTENT

Data analysis:

time series

Bayesian filtering

Pattern recognition

Prediction from data:

regularization theory

neural networks

Bayesian approaches

deep learning

RECOMMENDED READING/BIBLIOGRAPHY

Handouts

TEACHERS AND EXAM BOARD

Exam Board

FEDERICO BENVENUTO (President)

MICHELE PIANA (President)

SABRINA GUASTAVINO

LESSONS

LESSONS START

To be decided

Class schedule

The timetable for this course is available here: Portale EasyAcademy