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 :
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”.
Content
- 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)
Online resources
Bibliography
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
Faculty or entity
ELEC
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
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