2018-2019 / INFO8004-1

Advanced Machine learning


30h Th, 5h Pr, 45h Proj.

Number of credits

 Master in data science (120 ECTS)5 crédits 
 Master in electrical engineering (120 ECTS)5 crédits 
 Master of science in computer science and engineering (120 ECTS)5 crédits 
 Master in data science and engineering (120 ECTS)5 crédits 
 Master in computer science (120 ECTS)5 crédits 


Pierre Geurts, Gilles Louppe, Louis Wehenkel



Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

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). 

Learning outcomes of the learning unit

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.

Prerequisite knowledge and skills

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.

Planned learning activities and teaching methods

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 of delivery (face-to-face ; distance-learning)


Recommended or required readings

See course web-page:  http://www.montefiore.ulg.ac.be/~geurts/AML.html

Assessment methods and criteria

The students will carry out a mandatory reading assignment by groups of 4-6 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 two self-selected scientific papers. The presentation will be complemented by a written summary of those papers, in the format of a scientific technical report.

Work placement(s)


Organizational remarks

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)