Machine Learning : regression, dimensionality reduction and data visualization

lelec2870  2018-2019  Louvain-la-Neuve

Machine Learning : regression, dimensionality reduction and data visualization
5 credits
30.0 h + 30.0 h
Lee John (compensates Verleysen Michel); Verleysen Michel;
Main themes
Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.

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


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.


The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
  • Linear regression
  • Nonlinear regression with multi-layer perceptrons
  • Clustering and vector quantization
  • Nonlinear regression with radial-basis function networks
  • Probabilistic regression
  • Ensemble models
  • Model selection
  • Principal Component Analysis
  • Nonlinear dimensionality reduction and data visualization
  • Independent Component Analysis
  • Kernel methods
Teaching methods
Lectures, exercises, practical sessions on computers, project to be carried out individually of by groups of 2 students
Evaluation methods
Closed book oral examination, or written examination (depending on the number of students)
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
Faculty or entity

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

Title of the programme
Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Electrical Engineering

Master [120] in Agricultural Bioengineering

Master [120] in Chemistry and Bioindustries

Master [120] in Environmental Bioengineering

Master [120] in Forests and Natural Areas Engineering

Master [120] in Biomedical Engineering

Master [120] in Statistic: General

Master [120] in Computer Science

Master [120] in Mathematical Engineering

Master [120] in data Science: Statistic

Master [120] in data Science: Information technology