2020-2021 / INFO8004-1

Advanced Machine learning


25h Th, 45h Proj.

Nombre de crédits

 Master en science des données, à finalité5 crédits 
 Master : ingénieur civil électricien, à finalité5 crédits 
 Master : ingénieur civil en informatique, à finalité5 crédits 
 Master : ingénieur civil en informatique, à finalité (double diplômation avec HEC)5 crédits 
 Master : ingénieur civil en science des données, à finalité5 crédits 
 Master en sciences informatiques, à finalité5 crédits 
 Master en sciences informatiques, à finalité (double diplômation avec HEC)5 crédits 


Pierre Geurts, Gilles Louppe, Louis Wehenkel

Langue(s) de l'unité d'enseignement

Langue anglaise

Organisation et évaluation

Enseignement au deuxième quadrimestre


Horaire en ligne

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 to 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, including supervised learning, unsupervised learning and non-standard learning protocols such as semi-supervised learning, transfer learning or active learning. It will cover different families of techniques, such as neural networks, graphical models or kernel-based methods.
From the practical viewpoint, the emphasis will be on machine learning problems dealing with high-dimensional and structured representation spaces (e.g. texts, images, videos, time-series, and graphs). 

Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement

At the end of the class, the students are expected to master the state of the art in the field of machine learning. They will be able to implement, combine, or extend existing algorithms to address very complex machine learning tasks, and they will have the theoretical background to read scientific papers and start doing research in the field.

Savoirs et compétences prérequis

The course relies strongly on linear algebra, probability calculus, elementary statistics, and notions of optimization, as well as good knowledge of data structures and algorithms.
A prior introduction to machine learning, information theory, and graphical models, is highly recommended.
A strong interest in advanced applications of machine learning is expected from the students, as well as 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)


Adaptations organisationnelles liées au contexte sanitaire

Lectures recommandées ou obligatoires et notes de cours

See course web-page: https://github.com/glouppe/info8004-advanced-machine-learning 

Modalités d'évaluation et critères

The students will carry out a mandatory reading and presentation assignment by groups of 3 students. It will consist in reading recent research papers and presenting them during a lecture to the rest of the students.
The oral exam will consist in the presentation and in the critical summary of a self-selected scientific paper. The presentation will be complemented by a written summary, in the format of a scientific technical report.

  • Exam: 60%
  • Reading and presentation assignment: 40%



Remarques organisationnelles

This course is organized in full-English mode.


Teachers: Profs. Pierre Geurts (p.geurts@uliege.be), Gilles Louppe (g.louppe@uliege.be) and Louis Wehenkel (l.wehenkel@uliege.be)