Teaching:

Throughout my PhD, i had the opportunity to teach on a broad spectrum of subjects. In the list below, let the letters TD, TP and CM denote tutorials, lab sessions and lectures, respectively.

• Outils d'optimisation pour les sciences des données et de la décision (TD)
Taught to second year master students. Content of the course includes: convex optimization, gradient descent, stochastic gradient descent, ...
• Algorithmique et programmation 3 (TD, TP)
Aimed at second year bachelor students, these courses focus on finding the fastest algorithms to solve a given problem.
Programming language: Python.

• Fondements du Machine Learning (TD)
Aimed at third year bachelor students, these courses teaches the basics of machine learning: singular value decomposition (SVD), principal component analysis (PCA), ...
• Machine Learning et applications (TP)
Aimed at Master students, the purpose of this course is to learn how to code basic Machine learning algorithms (k-NN, ...) using Python.
• Pré-rentrée Calcul (CM,TD)
Aimed at first year bachelor students. The content of the course includes: chain rule, basic properties of integration, trigonometric and hyperbolic functions, binomial coefficients, ...
• Analyse de données (TD)
Aimed at third year bachelor students to make them learn the basics of statistics analysis.
• Algorithmes dans les graphes (TD, TP)
Aimed at third year bachelor students to teach same classic algorithms related to graphs theory, such as breadth-first search and depth-first search.

Prior to pursuing my doctoral studies in computer science, I obtained the Agrégation in mathematics. In consequence, I taught mathematics at a high school level in Collège Jean Jaurès as well as in Lycée Jean Monnet.