Applied Multi-output Machine Learning - January 2021

Length: 16 hours - 3 cfu

Abstract

Multi-output learning is grounded on simultaneously predict multiple outputs given an input. Its modelling algorithms are very important to support decision-making, since making decisions in the real world often involves multiple complex factors and criteria. Beyond classification and regression solutions, Multi-Output research area deals with all steps of a Data Mining pipeline, e.g. selecting features with a multiple output constraint.

Target: To introduce students to classic and state-of-the-art algorithms of Multi-Output based on applications and real-life case studies, as well as general questions related to analyzing and handling datasets with several outputs.

Method: The course is split between theoretical foundations and practical exercises:

1. Introduction to Multi-Output Learning
2. Multi-label problems (Classification Scenario)
3. Multi-target problems (Regression Scenario)
4. Mining multi-output scenarios

The practical exercises ask students to write original programs, as well as modify pre-coded examples in R or Python. Each meeting provides 4 hours of a subject.

Exame: The student needs to deliver a Multi-Output project (prototype level) with preliminary discussion and insights.

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Lecturer:

Prof. Barbon Junior Sylvio - Universidade Estadual de Londrina, Brazil

Prof. Paolo Ceravolo- Dipartimento di Informatica

Assessor:

Prof. Barbon Junior Sylvio - Universidade Estadual de Londrina, Brazil

Prof. Paolo Ceravolo- Dipartimento di Informatica