Initial training (mainly at Toulouse University and ISAE-Supaero)

• 2018-19 to 2025-26: Statistical foundations of Machine Learning (15 hours / year). Master 2 degree at ISAE/Supaero (github).


• 2017-18 to 2025-26: Image Analysis (16 hours / year). Master 2 in Mathematical Engineering. Toulouse University.


• 2017-18 to 2025-26: GPU computing with CUDA (6 hours / year). Master 2 degree at ISAE/Supaero.


• 2019-20 to 2024-25: Machine Learning (30 hours / year). International master course of Ecole Polytechnique (Palaiseau, France) given at Mohammed VI Polytechnic University (Ben Guerir, Morocco).


• 2023-24: Machine Learning (8 hours). M1 students of the Toulouse Graduate School of Earth and Space Science (EUR TESS). Toulouse University.


• 2018-19: Convex optimization (12 hours). Master 1 degree at Toulouse School of Economics (TSE).


• 2017-18, 2018-19: Statistics (8 hours / year). Master 2 degree at ISAE/Supaero.


• 2016-17: Lectures and practical courses in Image Analysis (8 hours). Master 2 in Mathematical Engineering. Université Paul Sabatier.


• 2016-17: Practical courses in Statistics (16 hours). Master 2 degree at ISAE/Supaero.


• 2005-06: Tutorials in Stochastic Process applied to heterogeneous media (16 hours). Master 2 degree in Mechanics. Université Paul Sabatier.


• 2005-06: Practical courses in Fluid Mechanics (18 hours). Preliminary degree in Mathematics and Computer Science applied to Sciences. Université Paul Sabatier.
  


• 2003-04 to 2005-06: Lectures and practical courses in Computational Fluid Mechanics (32 to 44 hours / year). My lecture notes about numerical methods for non-linear PDEs in 1D and 2D domains are here. Year project in Numerical simulation can also be found here. Masters 1 degree in Mechanics. Université Paul Sabatier.


• 2003-04 to 2004-05: Tutorials in Point Mechanics (20 hours / year). Preliminary degree in Mathematics and Computer Science applied to Sciences. Université Paul Sabatier.
  

Ongoing training (mainly with CNRS-Formation-Entreprises and Fidle online sessions)

• 2025-26: Six times two days professional training sessions trustable predictive and generative AI. Sessions organized by CNRS-Formation-Entreprises and in the compagnies. In collaboration with J.M. Loubes (IMT/INRIA/UT/ANITI).


• June 2025: 6 hours training session on the trustable use of generative AI (in collaboration with Pr JM Loubes) for technical and administrative managers at Toulouse School of Economics.


• May 2025: 6 hours online training session on trustable AI (in collaboration with Pr JM Loubes). Session organized by CNRS-Formation-Entreprises.


• May 2025: Half-day training session in trustable AI. Session organized by CNRS-Formation-Entreprises at CNRS Paris Michel-Ange.


• 2024-25: Two talks/courses on neural networks, given in the fidle online sessions 2025 (in French - ANF CNRS). Organiser and 3rd orator of the sequence 4 (Laws and AI), and main orator of the sequence 7 (Mathematics of DNNs). All talks are available on youtube.


• 2023-24: Three talks/courses on neural networks, given in the fidle online sessions 2024 (in French - ANF CNRS). Orator in the sequence 2 (From data to models), sequence 4 (AI, Society, Laws and Ethics), and sequence 5 (Maths and DNNs). All talks are available on youtube.


• 2023-24: Three times two days professional training sessions in XAI and robust ML. Sessions organized by CNRS-Formation-Entreprises and in the compagnies. In collaboration with J.M. Loubes (IMT/UT3/ANITI).


• June 2023: XAI and algorithmic bias in AI. 2 days course for professionals with CNRS formation entreprises. In collaboration with J.M. Loubes (IMT/UT3/ANITI). All information is given here and there


• January to April 2023: Three talks/courses of 2 hours each on neural networks, given in the fidle online sessions 2023 (in French - ANF CNRS). Orator of sequence 4 (mathematical aspects of neural networks), sequence 07 (PyTorch), and sequence 14 (AI and law/ethics). All talks are available on youtube.


• January to April 2022: Three talks/courses of 2 hours each on neural networks, given in the fidle online sessions 2022 (in French). Orator of sequence 4 (mathematical aspects of neural networks), sequence 11 (PyTorch), and sequence 14 (AI and law/ethics).


• January 2018: Data analysis and Machine learning with Python using the Scikit-learn module - Intermediate level. One day with courses and practicals at Observatoire Midi-Pyrénées.


• April 2017: Python for Scientists - Intermediate level. Three days of courses and practicals at CNRS - Observatoire Midi-Pyrénées. Lecture notes (in French):
  • Session 1 (Python général),
  • Session 2 (Python pour le numérique),
  • Session 3 (Python packaging),
  • Session 4 (Python objet),
  • Session 5 (Divers).
  
  

Short courses and technical seminars

• May 2026: Introduction to Machine-Learning and Scikit-Learn. One day course given at Observatoire-Midi-Pyrénées.


• March 2026: Introduction to PyTorch. One day course given at Observatoire-Midi-Pyrénées and co-organized with AOC-CIMI team-project.


• March 2025: Generative models based on Denoising Diffusion Probabilistic Models. 40 minutes talk at the seminar statistics - engineering, gathering applied statisticians from different labs in Toulouse.


• October 2024: Reading group on Ho et al: Denoising Diffusion Probabilistic Model, NeurIPS2020. Talk given in the context of an ANITI (IMT/IRIT/TSE) reading group. (Slides, Simple DDPM from scratch notebook). See also Data Flowr lecture notes and MVA lecture notes on generative models for further high quality information.


• June 2024: An introduction to explainable AI. Talk given at a reading group of Observatoire-Midi-Pyrénées (Slides).


• May 2023: Introduction to Machine Learning and Deep Neural Networks. Two hours talk/course given at IRAP/OMP (Astrophysics and Planetology Research Institute of Toulouse).


• May 2023: Law and A.I.. One hour talk given in collaboration with Ronan Pons during the EFELIA spring school on AI and social sciences (link).


• May 2022: Introduction to Deep learning. 1 hour tutorial at JRES 2022 with J.-L. Parouty (CNRS Grenoble) and S. Arias (INRIA Grenoble).


• November 2021: Three explainability techniques in Machine Learning. 1 hour talk at the CIMI-AOC seminar (slides).


• October 2021: Algorithmic bias of machine learning models in natural langage processing. 40 minutes talk at a meeting of the GDR TAL. Talk in collaboration with F. Jourdan (IRIT/IMT/ANITI), J.M. Loubes (IMT/ANITI) and N. Asher (IRIT/ANITI).


• April 2021: Two talks/courses of 2 hours each, given in the fidle online sessions 2021 (given in French).
  • Course 1: Demystifying the mathematical aspects of neural networks (Sequence 8 -- slides, video)
  • Course 2: Introduction to PyTorch (Sequence 9 -- slides, video)
  
  


• March 2021: Explainability techniques for Black-Box decision rules in ML. Invited talk at Marseille Astrophysics Laboratory (LAM).


• January 2021: Introduction to Pytorch. Courses+practicals of three hours at JDEV-2020. Fully given online.


• November 2020: Introduction to Machine Learning. Two courses+practicals of three hours each with Alexandre Boucaud (CNRS Research Engineer, APS, Paris) at JDEV-2020. The first course mainly focuses on the data and the second one on the models. Fully given online.


• March 2020: Explainability and Fairness of Black-box decision rules in AI. 40 minutes talk at the seminar statistics - engineering, gathering applied statisticians from different labs in Toulouse.


• October 2019: Explainability and Fairness of Black-box decision rules in A.I. One hour talk at the DEVLOG ongoing training event APSEM2019 (Slides in French: Part 1, Part 2, Part 3).


• August 2019: Introduction to Machine Learning. Two hours course and practical at CIMI Recherche-MIDI Summer-camp.


• July 2019: 20 hours course and practicals at Master School on Data Science and Geometry.
  • Week 2: Ten hours course and practicals in Machine Learning (slides of day 1: Intro to M.L. / Intro to Python for M.L.).
  • Week 4: Ten hours practicals in (1) Optimal transport for histogram equalization in images, (2) Diffeomorphic image registration with LDDMM, and (3) Neural networks with PyTorch.


• November 2018: Introduction to Machine Learning. Two hours talk at the CNRS-INRA ongoing training event (action de formation) APSEM2018 (slides in French).


• November 2018: Using TPUs in Deep Learning. One hour seminar at the National Institute for Space and Aeronautics (ISAE-Supaero).


• March 18: Introduction to Machine Learning (18 hours). VNUHCM - University of Science, Ho-Chi-Minh city, Vietnam.


• January 2018: Introduction to GPU computing and OpenCL. Three hours seminar for Master students at the National Institute for Space and Aeronautics (ISAE-Supaero).


• July 2017: JDEV 2017 (Development days -- CNRS/INRIA/IRSTEA/INRA/IRD ongoing training event), Marseille. Lecture notes (in French):
  • T7 AP01 (Initiation à Python),
  • T7 A01 (Python Apprentissage),
  • T7 GT02 (Retour d'experience sur OpenCL),
  • T7 GT03 (Python Pandas vs R),
  • T7 plénière (Apprentissage statistique pour la recherche par les données).
  
  


• January 2017: A short introduction to GPU computing and OpenCL. Seminar statistics - engineering, gathering applied statisticians from different labs in Toulouse (slides in French).


• November 2016: Three applications of data mining. Seminar for Master students at Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et des Télécommunications (INP ENSEEIHT), Toulouse.


• March 2015: Mathematical Engineering, what is that? Seminar to explain to high school students what can be an engineer in applied mathematics -- Formation métier Activités (slides in French. See also the slides of S. Déjean or a document edited by ONISEP).


• October 2014: Introduction to Cytoscape for large graphs visualization and analysis. Seminar statistics - engineering, gathering applied statisticians from different labs in Toulouse (slides in French).


• July 2014: Introduction to R. Personnel from Université Paul Sabatier. (for more information, see Sébastien Déjean's webpage ).


• June 2014: Introduction to statistics. Personnel from Université Paul Sabatier. (for more information, see Sébastien Déjean's webpage ).


• January 2014: Introduction to Python for scientists (slides in French). Institut de Mathématiques de Toulouse.


• July 2013: Introduction to R. Personnel from Université Paul Sabatier.


• June 2013: Introduction to statistics. Personnel from Université Paul Sabatier.


• December 2013: Three applications of data mining. Seminar for Master students at Ecole Nationale Supérieure d'Electrotechnique, d'Electronique, d'Informatique, d'Hydraulique et des Télécommunications (INP ENSEEIHT), Toulouse.


• March 2013: Using SVN for collaborative projects. Seminar statistics - engineering, gathering applied statisticians from different labs in Toulouse.

Scientific popularization

• May 2025: Understanding predictive and generative AI. One hour talk given for CNRS IRPS network (safety engineers).


• May 2025: A brief introduction to AI. Twenty minutes talk given for Envirobat Occitanie.


• March 2025: A brief introduction to AI. Twenty minutes talk given for administrive personnel at COMUE/Univ Toulouse


• June 2024: Detecting and tackling algorithmic biases in AI. Talk given for the SCID network.


• March 2024: XAI and algorithmic bias in AI. Talk given for the PEPR M4DI.


• March 2024: Towards trustable and legally compliant IA. Two talks organized by Club CNRS Entreprises, the first one being for CTOs and CEOs, and the second one to polititians and civil society representatives. In collaboration with J.M. Loubes (IMT/UT3/ANITI). Picture 1 and picture 2.


• March 2023: Towards trustable A.I.. Talk given in collaboration with Jean-Michel Loubes as a CNRS Formation Entreprise webminar. Talk available on youtube.


• February 2023: Law and A.I.. Talk given in collaboration with Ronan Pons as a Citoy'Liens seminar. Talk available on youtube: part 1 and part 2.


• November 2022: Algorithmic Bias and explainability in Machine Learning. 40 minutes talk at Phimeca seminar (link).


• October 2022: Research in machine learning and data/software licences. 20 minutes talk at DataNoos symposium (link).


• September 2022: Algorithmic Bias in Artificial Intelligence. 1h talk at CBI symposium (link).


• June 2022: Algorithmic Bias in Artificial Intelligence. 40 minutes talk at LVMH HR seminar.


• September 2021: Explainable AI and the future legal regulation of AI in the European Union. 50 minutes talk at the ECF national congress. Talk given in collaboration with R. Pons (Law dept of Univ. Toulouse/ANITI) to accountants (picture).


• March 2020: Explainability and Fairness of Black-box decision rules in AI. One hour talk with Ronan Pons (PhD student ANITI) at the Meetup Machine Learning Pau (slides in French).


• March 2020: Introduction to Machine Learning. One day ongoing training event for high school lecturers in Toulouse area (course + practicals in Python). Event managed by Yohann Genzmer (lecturer IMT).