Machine learning : regression, deep networks and dimensionality reduction

lelec2870  2021-2022  Louvain-la-Neuve

Machine learning : regression, deep networks and dimensionality reduction
5.00 credits
30.0 h + 30.0 h
Main themes
Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.
Learning outcomes

At the end of this learning unit, the student is able to :

1 With respect to the AA referring system defined for the Master in Electrical Engineering, the course contributes to the develoopment, mastery and assessment of the following skills :
  • AA1.1, AA1.2, AA1.3
  • AA3.1, AA3.2, AA3.3
  • AA4.1, AA4.2, AA4.4
  • AA5.1, AA5.2, AA5.3, AA5.5
  • AA6.3
At the end of the course, students will be able to :
- Understand and apply machine learning techniques for data and signal analysis, in particular for regression and prediction tasks.
- Understand and apply linear and nonlinear data visualization techniques.
- Evaluate the performances of these methods with appropriate techniques.
- Choose between existing methods on the basis of the nature of data and signals to be analyzed.
  • Linear regression
  • Nonlinear regression with multi-layer perceptrons (MLP)
  • Deep learning (convolutional CNN and adversarial GAN)
  • Clustering and vector quantization
  • Nonlinear regression with radial-basis function networks (RBFN)
  • Model selection
  • Feature selection
  • Principal Component Analysis (PCA)
  • Nonlinear dimensionality reduction and data visualization
  • Independent Component Analysis (ICA)
  • Kernel methods (SVM)
Teaching methods
Ex-cathedra course organized physically if sanitary conditions permit, and broadcasted or recorded if required by sanitary rules.  Practical sessions on computers, and project to be carried out individually or by groups of 2 students.
Evaluation methods
The assessment consists of two parts.
1) An assignment to be completed during the semester, which is the subject of questions in the examination;
2) An oral or written examination on the course and practical sessions.
Part 1) counts for 50% of the final assessment points, part 2) for 50%.
Students who have submitted a report on the assignment during the term and taken the examination in the January session may, on request, retain their points from part 1) for a possible examination in the August session.  Otherwise, students will be reassessed on part 1), on a new version of the assignment report submitted before the examination session, or by default on the initial version.
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
Teaching materials
  • slides disponibles sur Moodle - slides available on Moodle
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Statistics: General

Master [120] in Agricultural Bioengineering

Master [120] in Linguistics

Master [120] in Electrical Engineering [Version 2020]

Master [120] in Biomedical Engineering [Version 2020]

Master [120] in Computer Science [Version 2020]

Master [120] in Computer Science and Engineering [Version 2020]

Master [120] in Mathematical Engineering [Version 2020]

Master [120] in Data Science Engineering [Version 2020]

Master [120] in Data Science: Information Technology [Version 2020]

Master [120] in Environmental Bioengineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Chemistry and Bioindustries

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Data Science : Statistic