Linear and nonlinear data analysis methods, in particular for regression and dimensionality reduction, including visualization.
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 :
- AA1.1, AA1.2, AA1.3
- AA3.1, AA3.2, AA3.3
- AA4.1, AA4.2, AA4.4
- AA5.1, AA5.2, AA5.3, AA5.5
- AA6.3
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”.
Closed book oral examination, or written examination (depending on the number of students)
Lectures, exercises, practical sessions on computers, project to be carried out individually of by groups of 2 students
- 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
Reference books (not mandatory) mentioned on the website of the course
