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.
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 (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)
Due to the COVID-19 crisis, the information in this section is particularly likely to change.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.
Due to the COVID-19 crisis, the information in this section is particularly likely to change.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.
- slides disponibles sur Moodle - slides available on Moodle