François HU

About me, my research, my teaching and my experimentations

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About me

I am a Data Scientist (PhD) and lecturer in machine learning and computational statistics at ENSAE, EPITA and Institut des Actuaires. In a nutshell…

Research

My topics of interest are the following :

  • ML in Insurance & Finance
  • ML fairness & transparency
  • NLP in ESG Reporting for Sustainable Finance
  • Semi-supervised learning & sampling methods

Recent papers

Recent talks

Teaching

EPITA - École pour l’informatique et les techniques avancées (2020 - …)

Master of Science :

Master of Science in Artificial Intelligence Systems :

  • Numerical Algorithms (and optimization for Machine Learning) by François HU
    • Lecture 1 : Calculus refresher [Lecture] [Notebook]
    • Lecture 2 : Unconstrained optimization [Lecture] [Notebook]
    • Lecture 3 : Constrained optimization [Lecture]
    • Lecture 4 : Numerical methods in linear algebra [Lecture]
    • Lecture 5 : Machine learning applications
    • Lecture 6 : [Oral presentations]-> Titanic challenge
    • Practical work : Linear/Logistic regression, PCA (and SVM) [Notebook]
  • Bayesian Machine Learning by François HU
    • Lecture 1 : Bayesian statistics [Lecture]
    • Lecture 2 : Latent Variable Models and EM-algorithm [Lecture]
    • Lecture 3 : Variational Inference and intro to NLP [Lecture]
    • Lecture 4 : Markov Chain Monte Carlo (& Gaussian Process) [Lecture]
    • Lecture 5 : [Oral presentations]-> Topic models, Bayesian optim, Uncertainty and t-SNE
    • Practical work 1 : Naive Bayes Classifier [Notebook]
    • Practical work 2 : GMM, Probabilistic K-means and PCA [Notebook]
    • Practical work 3 : Topic Modeling with LDA [Notebook]
    • Practical work 4 : Sampling posteriors with MCMC [Notebook]
    • Practical work 5 : Bayesian Linear Regression [Notebook]
    • Bonus points : p85-ex1 Lec1 (0.5pt); PW1 (1pt); PW2 (2pt); PW3 (0.5pt); PW4 (1pt); PW5 (1pt)

Institut des Actuaires - Formation Data Science pour l’Actuaire (2019 - …)

Teaching assistant

Institut polytechnique de Paris (ENSAE, Polytechnique) (2019 - …)

  • 1A - semester 1 (2019 - 2020) : Algorithme et programmation by Xavier Dupré
  • 2A - semester 2 (2019 - 2021) : Simulation et Monte Carlo by Nicolas Chopin
  • 2A - semester 2 (2019 - 2020) : Theoretical foundations of Machine Learning by Vianney Perchet
  • 3A - semester 1 (2020 - 2021) : Advanced Machine Learning by Vianney Perchet
    • This course is about ERM, SVM, Boosting, Neural Net and Optimization
    • Directed work : VC-dimension and ERM (correction soon available)
    • Practical work : Python, Linear Regression and SVM [Corr in Python]
    • Practical work : RKHS, optimization and neural networks [Written corr] [Neural Nets in python]
  • 3A - semester 2 (2019 - 2020) : Machine Learning for finance by Romuald Elie
    • Speaker in NLP