Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 30.0 h
Q1
Teacher(s)
Lee John (compensates Verleysen Michel); Verleysen Michel;
Language
English
Main themes
Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.
Aims
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. |
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”.
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
Lectures, exercises, practical sessions on computers, project to be carried out individually or by groups of 2 students
Evaluation methods
Closed book oral examination, or written examination (depending on the number of students). The project is part of the evaluation.
Online resources
Bibliography
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
Teaching materials
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
- slides disponibles sur Moodle
Faculty or entity
ELEC
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Chemistry and Bioindustries
Master [120] in Forests and Natural Areas Engineering
Master [120] in Biomedical Engineering
Master [120] in Agricultural Bioengineering
Master [120] in Data Science : Statistic
Master [120] in Mathematical Engineering
Master [120] in Computer Science and Engineering
Master [120] in Electrical Engineering
Master [120] in Computer Science
Master [120] in Data Science Engineering
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Data Science: Information Technology
Master [120] in Statistic: General
Master [120] in Environmental Bioengineering