2022-2023
- Privacy Preserving Machine Learning, Master 2 Data Science, University of Lille
Course on private machine learning with a focus on differential privacy. 24 hours of lectures and lab sessions (taught in English).
[Course page]
- Introduction to Privacy Preserving Machine Learning, Ecole Centrale de Lille
Short version of my course on private machine learning with a focus on differential privacy. 6 hours of lectures and lab sessions (taught in French).
2021-2022
- Privacy Preserving Machine Learning, Master 2 Data Science, University of Lille
Course on private machine learning with a focus on differential privacy. 24 hours of lectures and lab sessions (taught in English).
[Course page]
- Introduction to Privacy Preserving Machine Learning, Ecole Centrale de Lille
Short version of my course on private machine learning with a focus on differential privacy. 6 hours of lectures and lab sessions (taught in French).
2020-2021
2019-2020
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 18 hours of lectures and lab sessions (taught in French).
- Certificat d’Études Spécialisées Data Scientist, Télécom Paris
Introduction to supervised learning, Support Vector Machines. 14 hours of lectures and lab sessions (taught in French).
- Machine Learning, Data Analysis & Decision making, Ecole Centrale de Lille
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 12 hours of lectures and lab sessions (taught in French).
2018-2019
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 18 hours of lectures and lab sessions (taught in French).
- Certificat d’Études Spécialisées Data Scientist, Télécom Paris
Introduction to supervised learning, Support Vector Machines. 14 hours of lectures and lab sessions (taught in French).
- Machine Learning, Data Analysis & Decision making, Ecole Centrale de Lille
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 12 hours of lectures and lab sessions (taught in French).
2017-2018
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 15 hours of lectures and lab sessions (taught in French).
- Certificat d’Études Spécialisées Data Scientist, Télécom Paris
Introduction to supervised learning, Support Vector Machines. 10.5 hours of lectures and lab sessions (taught in French).
- Machine Learning, Data Analysis & Decision making, Ecole Centrale de Lille
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 12 hours of lectures and lab sessions (taught in French).
2016-2017
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : metric learning, large-scale kernel methods, graph-based learning. 18 hours of lectures and lab sessions (taught in French).
- Certificat d’Études Spécialisées Data Scientist, Télécom Paris
Introduction to supervised learning, Support Vector Machines. 14 hours of lectures and lab sessions (taught in French).
2015-2016
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : distributed optimization, metric learning, large-scale kernel methods, graph-based learning. 21 hours of lectures and lab sessions (taught in French) and organization of a data challenge with the company Morpho on the topic of rank aggregation for facial recognition.
- Certificat d’Études Spécialisées Data Scientist, Télécom Paris
Introduction to supervised learning, Support Vector Machines. 10 hours of lectures and lab sessions (taught in French).
2014-2015
- Advanced Machine Learning, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Advanced machine learning methods : stochastic approximation, distributed algorithms, structured prediction, large-scale kernel methods. 15 hours of lab sessions (taught in French) and organization of a data challenge with the company Morpho on the topic of metric learning for facial recognition.
- Machine Learning and Data Mining, Cycle Master et Master Spécialisé Big Data, Télécom Paris
Introduction to machine learning and data mining : Python programming, k-nearest neighbors, perceptron, classification and regression trees, large margin and kernel methods, ensemble learning and unsupervised learning. 24 hours of lab sessions, taught in French.
2012-2013
- Design and Analysis of Algorithms, Master Erasmus-Mundus CIMET, University of Saint-Etienne
Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.
2011-2012
- Pattern Recognition, Master Erasmus-Mundus CIMET, University of Saint-Etienne
Introduction to convex optimization, support vector machines (SVM) and their applications. 20 hours of lectures, tutorials and lab sessions, taught in English. In charge of the course unit.
- Design and Analysis of Algorithms, Master Erasmus-Mundus CIMET, University of Saint-Etienne
Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.
- Object-Oriented Programming, Licence professionnelle ATII par alternance, IUT de Saint-Etienne
Introduction to object-oriented programming and Java. 21 hours of lectures, tutorials and lab sessions taught in French to students in cooperative education (work-based learning). In charge of the course.
- Introduction to Office Tools, L1 Sciences Technologie Santé, University of Saint-Etienne
Introduction to information technology : Internet, word processing, HTML, spreadsheet and presentation applications, databases. 12 hours of lab sessions, taught in French.
2010-2011
- Design and Analysis of Algorithms, Master Erasmus-Mundus CIMET, University of Saint-Etienne
Introduction to C programming, divide-and-conquer algorithms, dynamic programming, graph algorithms. 22 hours of lab sessions with student projects, taught in English.
