Introduction au Machine Learning

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BROCHURE

academic year

2024-2025

Course teacher(s)

Sébastien DE VALERIOLA (Coordinator)

ECTS credits

Language(s) of instruction

french

Course content

Machine learning is now an essential component of the quantitative methods available to industry professionals and researchers. These techniques enable large-scale data analysis, leading to accurate predictions and informed decisions in a variety of fields. Whereas they were difficult to access a decade ago, today they are easily deployable even in limited environments, such as on a personal computer. Although these methods are based on solid foundations in advanced statistics and computer science, they are now accessible to anyone with a basic understanding of quantitative methods and algorithms. This course offers a theoretical and practical overview of these methods.

Objectives (and/or specific learning outcomes)

The course aims to demystify machine learning by giving students a theoretical and practical grounding in machine learning methods.
At the end of the course, students should be familiar with the main families of machine learning methods, understand their challenges, possibilities and limitations, and be able to implement them in R and interpret the results.

Teaching methods and learning activities

The course alternates between theoretical presentations and practical application (in R) of the concepts and models covered.

Course notes

Université virtuelle

Other information

Contacts

Sébastien de Valeriola (sebastien.de.valeriola@ulb.be)

Campus

Solbosch

Evaluation

Method(s) of evaluation

Oral presentation

Oral presentation

The evaluation consists of an oral presentation of the results of a data analysis chosen by the student (from a set provided by the teacher), prepared in advance. During the presentation, students are asked questions relating to their understanding of the concepts and models used, either in direct relation to the analysis carried out, or more generally in relation to the content covered in the course.

With regard to the use of generative artificial intelligence tools, the terms and conditions of this course follow those set out for dissertations and TPMs in the Master "dissertation guide".

Language(s) of evaluation

french