Objectifs
At the end of this module, the student should have understood and be able to explain (main concepts):
1.The approximation of data using splines, either through interpolation or smoothing.
2.The link with geometry generation in CAD and the capabilities for image processing.
3.Automatic differentiation and the structure of a neural network.
4.Object-oriented programming in Python.
The student should be able to:
1.Determine and calculate the interpolation spline, the smoothing spline, and the least squares spline for nn points.
2.Construct a B-Spline curve for nn points and a B-Spline surface.
3.Interpolate and filter an image using splines.
4.Design a basic neural network.
5.Develop an automatic differentiation library in Python.
Pré-requis
Multivariable function differentiation, unconstrained optimization (existence, first-order Euler equations, gradient algorithms), linear algebra (matrix systems, scalar product, adjunction). Strong knowledge of Python.
Évaluation
L’évaluation des acquis d’apprentissage est réalisée en continu tout le long du semestre. En fonction des enseignements, elle peut prendre différentes formes : examen écrit, oral, compte-rendu, rapport écrit, évaluation par les pairs…
