2024-2025 / INFO8004-1

Advanced Machine learning

Duration

25h Th, 20h Proj.

Number of credits

 Master MSc. in Computer Science, professional focus in computer systems security5 crédits 
 Master MSc. in Data Science, professional focus5 crédits 
 Master MSc. in Electrical Engineering, professional focus in electronic systems and devices5 crédits 
 Master Msc. in electrical engineering, professional focus in "Smart grids"5 crédits 
 Master MSc. in Data Science and Engineering, professional focus5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in management5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems5 crédits 
 Master Msc. in computer science and engineering, professional focus in intelligent systems (double diplômation avec HEC)5 crédits 
 Master MSc. in Computer Science, professional focus in management5 crédits 
 Master Msc. in Electrical Engineering, professional focus in Neuromorphic Engineering5 crédits 
 Master MSc. in Computer Science and Engineering, professional focus in computer systems and networks5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems5 crédits 
 Master MSc. in Computer Science, professional focus in intelligent systems (double diplômation avec HEC)5 crédits 

Lecturer

Pierre Geurts, Gilles Louppe, Louis Wehenkel

Coordinator

Gilles Louppe

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

Schedule

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 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.

Learning outcomes of the learning unit

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.

Prerequisite knowledge and skills

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.

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, hybrid learning)

face-to-face

Slides and materials will be made publicly available on GitHub during the semester.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report

Continuous assessment


Additional information:

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%

Work placement(s)

None

Organisational remarks and main changes to the course

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)

Association of one or more MOOCs