30.0 h + 30.0 h
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 :
- 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)
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.
Closed book hybrid written-oral exam. The project is part of the evaluation. Examination modalities may be adapted according to sanitary conditions and to the number of registered students.
Divers livres de références (mais non obligatoires) mentionnés sur le site du cours
- slides disponibles sur Moodle - slides available on Moodle
Faculty or entity
Title of the programme
Master  in Linguistics
Master  in Statistics: General
Master  in Agricultural Bioengineering
Master  in Data Science Engineering
Master  in Electrical Engineering
Master  in Environmental Bioengineering
Master  in Computer Science and Engineering
Master  in Data Science: Information Technology
Master  in Biomedical Engineering
Master  in Forests and Natural Areas Engineering
Master  in Chemistry and Bioindustries
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master  in Computer Science
Master  in Mathematical Engineering
Master  in Data Science : Statistic