5.00 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John; Verleysen Michel;
Language
English
> French-friendly
> French-friendly
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 :
- 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. |
Content
- 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 taken the examination in the January session may, on request, retain their points from part 1) for a possible examination in the August session.
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 taken the examination in the January session may, on request, retain their points from part 1) for a possible examination in the August session.
Online resources
Bibliography
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
ELEC
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Data Science : Statistic
Master [120] in Biomedical Engineering
Master [120] in Forests and Natural Areas Engineering
Master [120] in Linguistics
Master [120] in Environmental Bioengineering
Master [120] in Electrical Engineering
Master [120] in Statistics: General
Master [120] in Chemistry and Bioindustries
Master [120] in Computer Science and Engineering
Master [120] in Computer Science
Master [120] in Mathematical Engineering
Master [120] in Data Science Engineering
Certificat d'université : Statistique et science des données (15/30 crédits)
Master [120] in Agricultural Bioengineering
Master [120] in Data Science: Information Technology