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 objective of the course is to enable students to:

• understand data of various types (e.g., data extended in time and/or space) as vectors or matrices;

• process data through linear and nonlinear operations;

• understand and define relationships between data as mathematical operations between vectors;

• comprehend qualitative descriptive models of data;

• understand the approach of learning from examples in the absence of a qualitative model of the data;

• formulate optimization problems in order to solve numerical problems arising from real-world scenarios, such as:

  • classification problems

  • regression problems

By the end of the course, students will be able to navigate the field of computational data analysis and understand some aspects of artificial intelligence. They will also have gained familiarity with computational tools for data processing aimed at prediction.

PREREQUISITES

Python programming

Data formats and I/O issues

Basic aspects of numerical analysis and statistics (generalized linear models)

TEACHING METHODS

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

Students with valid certifications for Specific Learning Disorders (SLDs), disabilities or other educational needs are invited to contact the teacher and the School's contact person for disability at the beginning of teaching to agree on possible teaching arrangements that, while respecting the teaching objectives, take into account individual learning patterns. Contacts of the teacher and the School's disability contact person can be found at the following link: https://unige.it/commissioni/comitatoperlinclusionedeglistudenticondisabilita

SYLLABUS/CONTENT

Data analysis:

time series

Bayesian filtering

Pattern recognition

Prediction from data:

regularization theory

neural networks

Bayesian approaches

deep learning

RECOMMENDED READING/BIBLIOGRAPHY

Students will be provided with slides and guidelines for the lab exercises

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Second semester

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Oral exam and submission of laboratory assignments

ASSESSMENT METHODS

The evaluation consists of laboratory assignments and an oral exam. No written midterm tests are scheduled.

The oral exam will mainly cover the theoretical topics presented by the instructors, with the goal of assessing the student's understanding. This will include the discussion and intuitive justification of analytical concepts as well as applied examples.

The discussion of the laboratory work will focus primarily on the functioning of the implemented code in each assignment and on the interpretation of the results obtained.

FURTHER INFORMATION

Prerequisites:
The only substantial prerequisites are a basic knowledge of Python, the main data formats, and fundamental concepts of numerical analysis and statistics.

Attendance modality:
In-person attendance (strongly recommended)

Exam registration:
To be arranged with the instructor