Main teaching duties

2024--Currently: Professeur attaché (adjunct Professor) of AI at Université Paris-Saclay, teaching in the Master of Science in AI and in the Master of Science in Data Science of the SaclAI-School of UPSaclay: 2022--Currently: Co-responsable scientifique côté SystemX du MS IAC -- Mastère Spécialisé en Intelligence Artificielle de Confiance, co-porté avec Centrale Supélec From 2016 to 2022: Professor of Applied Mathematics, teaching at the maths and computer science departement of Caen University:
  • Director of the Master 2 degree in Statistics and Decision Analytics (SAD), from 2017 - 2019
  • Member of the council of the department of Mathematics and Computer Science
From 2011 to 2016: A/Professor of Computer Science, teaching at the computer science departement of Toulon University:
  • Director of the Bachelor degree in Engineering sciences from 2012 to 2015
  • Responsible of the second year of the Bachelor degree in Engineering sciences
  • Responsible of the tutoring in computer science at the faculty of science at Toulon university
From 2007 to 2011: Instructor and ATER of Computer Science, teaching at the computer science departement of Paris 13.

Responsible and teacher of the following courses from 2016 to 2022 (Average of 275+ teaching hours per year) :
  1. Probabilities, Statistics 3 (Bsc in Maths & Applied Maths)
  2. Data Analysis 1 (Bsc in Maths & Applied Maths)
  3. Data Analysis and Classification (Master 1 Statistics & Master 1 Computer Science)
  4. Statistical Learning (Master 2 Statistics & Master 2 Computer Science)
  5. Time series Analysis (Master 2 Statistics)
Responsible and teacher of the following courses (200+ teaching hours per year) : (2011-2016)
  1. Programming (Language C and Python) (Bsc Computer Science)
  2. Probabilities (Bsc Computer Science)
  3. Inferential Statistics (Master 1 Computer Science)
  4. Statistical Learning and Data Analysis (Master 2 Computer Science)

TC2: Optimization for Machine Learning

Master of Science in AI and Master of Science in Data Science @ Université Paris-Saclay

Optimization for Machine Learning
Course Slides
  • week1: Introductions and background: [pdf Slides]
  • week2: Continuous optimization formulation and basic concepts; Optimization concepts over multivariate continuous spaces; Convexity and Differentiability; Gradient and Hessian concepts; Examples in Machine Learning [pdf Slides], TD, TP
  • week3: continuous optimizaiton (Gradient descent methods) : Mathetamatical construction of descent methods, Gradient Descent, Descent Directions, Convergence and Convergence rates; Step-size tuning and Line Search; Accelerations [pdf Slides], TD , TP
  • week4: Continuous optimization (Second order methods : Newton methods including Quasi-Newton, secant, IRLS); and mid-term exam [pdf Slides], TD , TP
  • week5: constrained optimizaiton: Equality and Inequality constraints, Duality/Lagrangian, KKT optimality conditions; Examples in ML [pdf Slides], TD , TP
  • week6: Stochastic optimization, Non-convex optimization (Stochastic Gradient, The EM Algorithm); Examples in ML [pdf Slides], TD
Practical sessions TD-TP Exams
  • Mid-Term Exam (cc): week4, November 28th, 2024, 16h15-17h00. [CC]
  • Final Exam (CT): week7, December 19th, 2024. [CT]

T3A: Machine Learning Algorithms

Master of Science in AI and Master of Science in Data Science @ Université Paris-Saclay

T3A: Machine Learning Algorithms
Course Slides
  • Week4: Probabilistic framework for machine learning: Discriminative vs Generative learning, Empirical Risk Minimization, Risk Decomposition, Bias-Variance Tradeoff; Maximum Likelihood Estimation (MLE), MLE and OLS in regression, MLE and IRLS in softmax classification
  • Week5: Unsupervised Learning and Clustering: K-means, Mixture Models, EM algorithms,..
  • Week6: Unsupervised Learning and Dimensionality reduction: PCA, Probabilistic PCA & EM, ICA,…
    • Unsupervised Learning: Dimensionality Reduction: PCA, PPCA, EMPPCA: [pdf Slides]
    • Unsupervised Learning: Dimensionality Reduction: tSNE: [tSNE-en] [tSNE-fr]

Practical Sessions Exam
  • Final Exam (CT): week7, february 20, 2025. [CT]

Statistical Learning

MS IAC -- Mastère Spécialisé en Intelligence Artificielle de Confiance @Centrale Supélec

Part 1 Part 2
  • Part 3
    • Unsupervised Learning
      • Slides TBA
      • Practical sessions TBA

    • Master SAAD

    Présentation du Master SAAD

    Master 2 Statistics & Decision Analytics / Master 2 Computer Science : Probabilistic Learning and Data Analysis (CM/TP)

      [Slides (all)]

      Slides per topic:
    • Review of probability and statistics (in french)[ pdf]
    • Review of parameter estimation - Maximum Likelihood, OLS, Regression... (in french) [pdf]
    • Introductionary concepts, Pattern recognition, Machine Learning [pdf]
    • Supervised Learning: Classification (KNN, Gaussian Discriminant Analyses, Logistic/Softmax regression) [pdf]
    • Unsupervised learning: Clustering, Kmeans, Mixtures, Model Selection [pdf]
    • Unsupervised learning: Topographic Learning (SOM, GTM, GTM through time)[pdf]
    • Unsupervised learning: Sequential Data Modeling, Markov Chains, HMMs, Baum-Welch (EM), Viterbi[pdf]
    • Unsupervised learning: Dimensionality reduction (PCA, Probabilistic PCA, Factor Analysis, EM)[pdf]
      Practical work:
    • Gaussian Discriminant Analysis and Logistic regression (supervised learning)[pdf] [solution]
    • KNN (tp)[pdf]
    • Supervised learning, Tp (LDA-QDA)[pdf] [pdf]
    • Unsupervised learning: Kmeans and xEM clustering[pdf]
    • Kmeans (TP)[pdf]
    • EM-GMM (TP)[pdf]
    • Sequential Data Modeling (HMMs)[pdf] [solution]
    • Principal Component Analysis and Classification (TP, ACP- faces images)[pdf]
    • Regression Mixtures and EM (TP) [pdf]

    Master 2 Statistics & Decision Analytics : Time series clustering and segmentation (CM/TD/TP)

    [related pdf slides]

    Licence 3 Mathematics and applied Mathematics (MIASHS) / Master 1 Stat/CS: Data Analysis (CM/TD/TP)


    Licence 3 Mathematics and Applied Mathematics (MIASHS): Probabilités et statistique (CM/TD)


    Licence 2 Mathematics : Probabilités et statistique (TD)


    Past: at Toulon University

    [2011/2012] [2012/2013] [2013/2014] [2014/2015][2015]
    • Master 2 Informatique : Probabilistic Learning and Data Analysis (D33) [PDF] Slides
        • Slides
      • Review of probability and statistics [PDF]
      • Parameter estimation - Maximum Likelihood method, ... [PDF]
      • Pattern recognition an machine learning concepts; Classification (discrimination), ... [PDF]
      • Mixture models, GMM and EM - KmeansClustering - Model-Based Clustering, ... [PDF]
      • SOM, GTM, GTM Through Time [PDF]
      • Markov Chains, HMMs, Baum-Welch (EM), Viterbi [PDF]
      • PCA, PPCA, FA [PDF]
    • Master 2 Informatique : Initiation à la recherche (Initiation to research)
      • Studied Paper: Miin-Shen Yang, Chien-Yo Lai, Chih-Ying Lin. "A robust EM clustering algorithm for Gaussian mixture models", Pattern Recognition , Volume 45, Issue 11, November 2012, Pages 3950–3961 [pdf]
        • Solution: was provided by e-mail (please see your email box)
    • Master 1 Informatique : Eléments de statistique Inférentielle (Elements of inferential statistics) (d13)   [slides pdf]    [Polycopié]
        • Slides (in French) :
      • review of probability and stat, Random vectors, Gaussian vectors, Central Limit thm, etc [PDF]
      • Estimation theory, Estimators properties, Cramér-Rao Lower Bound, estimation methods [PDF]
      • Maximum Likelihood Estimator (MLE) [PDF]
      • Least Squares Estimators (LSE), Linear regression [PDF]
      • Interval estimation, Confidence intervals [PDF]
      • Hypothesis Testing [PDF]
    • Licence 2 Sciences pour l'Ingénieur : Programmation II – Langage C (C programming ) (I41)
      • Polycopié : [pdf]
    • Licence 2 Sciences pour l'Ingénieur : Probabilités discètres (Discrete Probability) (MI48)
      • Polycopié : [pdf]
    • Licence 1 Sciences pour l'Ingénieur : Algorithmique + programmation Python (Algorithmics + Python programming ) (I11 ; I21)

    Past at Paris 13 University :

    [2007-2011]
    • Temporary Research and Teaching Assistant (ATER) (march 2010 - July 2011)
      • Mathématiques et informatique
      • Introduction Structures de Données Linéaires
      • Algorithmique des Structures de Données Linéaires
      • Programmation Impérative
      • Programmation Orientée Objet (Java)
      • Algorithmique et Arbres
    • Teaching Assistant (Moniteur) (2007-2010)
      • C Programming Langage (64h/year)
      • XHTML (32h/year), Introduction to GTK (32h/year)