Durée
25h Th, 20h Proj.
Nombre de crédits
Enseignant
Coordinateur(s)
Langue(s) de l'unité d'enseignement
Langue anglaise
Organisation et évaluation
Enseignement au deuxième quadrimestre
Horaire
Unités d'enseignement prérequises et corequises
Les unités prérequises ou corequises sont présentées au sein de chaque programme
Contenus de l'unité d'enseignement
The goal of this course is to prepare students for the study of state-of-the-art research in the field of machine learning.
The class will be organized as a journal club, with reading and presentation assignments of recent machine learning research papers.
In terms of content, this course will focus on advanced topics in machine learning, deep learning, and artificial intelligence.
Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement
At the end of the class, the students are expected to have acquired an overview of the state of the art in the field of machine learning. They will have the theoretical background to read and present scientific papers and to start doing research in the field.
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, II.2, III.1, III.2, V.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in data science and engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, IV.8, V.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in electrical engineering.
This course contributes to the learning outcomes I.1, I.2, II.1, II.2, III.1, III.2, V.2, VI.1, VI.2, VI.3, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.
Savoirs et compétences prérequis
We strongly recommend taking this course *after* Introduction to Machine Learning (ELEN0062) and Deep Learning (INFO8010). The course will cover advanced and state-of-the-art materials that assume a good prior knowledge of the foundations covered in ELEN0062 and INFO8010.
A strong interest in advanced applications of machine learning is expected from the students, as well as a willingness to self-learn in an autonomous way and to present their ideas in a clear fashion during the course lectures.
Activités d'apprentissage prévues et méthodes d'enseignement
This course, preparing to research, needs an active participation of the student. Ex-cathedra lectures given by the professors will be supplemented by discussion sessions with the students around key papers in the field, and by research seminars given by external speakers.
Personal student projects will consist in the critical reading, discussion and oral presentation of a selection of scientific papers on the topics related to the course.
Mode d'enseignement (présentiel, à distance, hybride)
face-to-face
Supports de cours, lectures obligatoires ou recommandées
Slides and materials will be made publicly available on GitHub during the semester.
Modalités d'évaluation et critères
Examen(s) en session
Toutes sessions confondues
- En présentiel
évaluation orale
Travail à rendre - rapport
Evaluation continue
Explications complémentaires:
The students will carry out a mandatory reading and presentation assignment in groups of 3. It will consist of reading recent research papers and presenting them to the rest of the students during a lecture.
The oral exam will consist of a self-selected scientific paper presentation and a critical summary.
Weighting:
- Exam: 60%
- Reading and presentation assignment: 40%
Stage(s)
None
Remarques organisationnelles et modifications principales apportées au cours
This course is taught in English.
Contacts
Teachers: Profs. Pierre Geurts (p.geurts@uliege.be), Gilles Louppe (g.louppe@uliege.be) and Louis Wehenkel (l.wehenkel@uliege.be)