## Adversarial attacks on deep learning and defenses

M2, *CentraleSupélec*, 2024

I am the coordinator of this module since 2024.

M2, *CentraleSupélec*, 2024

I am the coordinator of this module since 2024.

M2, *CentraleSupélec*, 2022

I was the coordinator of this module in 2022-2024. Besides, I teach the Flash backend framework.

M1, *CentraleSupélec*, 2020

I am in charge of this module since 2020.

M2, *CentraleSupélec*, 2020

I was the coordinator of this module in 2023-2024.

L3, *INSA-Toulouse*, 2018

This course presents some graph theory and their algorithms:

- Topological sort and level structure
- Breadth-first and depth-first search
- Shortest path algorithms: Dijkstra, Bellman-Ford
- Maximum flow / minimum cut with Ford-Fulkerson algorithm

L3, *University of Toulouse*, 2015

This course aims to train the students in general artificial intelligence techniques:

- Representation of problems with state diagrams
- Automatic resolution with search algorithms (breadth-first search, depth-first search, iterative depth-first search, best-first, greedy search and A*)
- Exploration of game tree (minimax algorithm)
- Graph coloration (Welsh and Power algorithm and DSatur)
- Resolution of constraint satisfaction problems (backtrack and forward checking algorithms)

M1, *UPSSITECH engineering school*, 2015

This course aims to train the students in machine learning techniques:

- Overview of various ML techniques. Supervised learning (decision tree learning, k-nn, SVM) and unsupervised learning.
- Experiments with Weka, a data mining tool
- Bayesian reasoning and learning
- Project on reinforcement learning: the students must develop an agent that learns to escape a maze with Q-learning.