Teaching activities
Most of my teaching activities are carried out at INSA Rouen Normandie, within the computer science and information technology department (ITI).
My teaching covers numerical analysis, algorithms, statistics, optimization, and machine learning, with a particular interest in keeping courses closely connected to current tools and research topics.
Here is a selection of the main courses and teaching activities.
Matrix factorization (LU, QR, Cholesky), linear systems, eigensystems, iterative methods, and scientific computing foundations.
- Algorithms and C Programming (coordinated by Nicolas Delestre)
C programming, data structures, dynamic memory, collections, and advanced algorithms.
Introduction to statistics and data analysis, including descriptive statistics, linear regression, and statistical significance testing.
- Machine Learning for Chemistry (Master 2, University of Rouen Normandy)
An introduction to machine learning for chemistry students, with a focus on data analysis and predictive methods relevant to chemical applications.
A course taught at the Franco-Azerbaijani University in Baku. It covers the foundations of machine learning, from basic statistics to supervised and unsupervised methods such as PCA, K-means, ridge regression, and SVMs. Course material can be freely downloaded here.
A teaching module on machine learning for graphs, developed for École Polytechnique for the Institut National Polytechnique Félix Houphouët-Boigny in Yamoussoukro, Côte d’Ivoire.
Feel free to contact me if you need course material or additional information.