Université Paris Cité, Machine Learning, Master M1 [2016-2022]

Data Science

• • • Exploratory analysis (R, Univariate/Bivariate analysis)

    • Introduction to classification

    • Multiple Linear Regression

    • Logistic Regression

    • Linear discriminant analysis

    • Decision tree

    • Boosting

    • Support Vector Machine

    • Cross-validation

• • • Final project (R / Python / notebooks)

Descriptive statistics and R ShinyApps

1. • • R and R Shiny programming

     • Exploratory analysis (R, Univariate/Bivariate analysis)

     • Application to churn prediction and analysis

     • Final project (R, R Shiny)

Université Paris Cité, Machine Learning, Master M2 [2017-2022]

Network reconstruction and analysis

1. • • Introduction to network models

     • Network reconstruction methods

     • PLS Path Modeling

     • Community detection

     • Final project (R / Python / notebooks)

Université Paris Cité, Machine Learning, Master M2 [2018-2022]

Deep Learning

• • • Introduction to deep learning (slides)

    • Optimizers and loss functions (slides)

    • Deep autoencoders (slides) (script1)

    • Text-Mining / Introduction to DL-NER (slides) (tutorials)

• • • Final project (Python / notebooks)

Université Paris Cité, Machine Learning, Master M1/M2 [2018-2022]

Python

• • • Introduction to Python

    • Introduction to Python for data science

    • Mini-project (Python / notebooks)

UPMC, Computer Science (Bioinformatics and Modelling), Master M1/M2 [2011-2014]

Statistics in bioinformatics and algorithms for sequences

Introduction to biology and algorithms for graphs in bioinformatics

Biological networks: reconstructions et analysis

Paris Descartes University, 3rd year - Computer Science [2016-2018]

• • Software engineering

Polytech'UPMC, 3rd year - Electronics and Computer Science [2011-2014]

• • Databases

  • Computer Science projects

Pierre and Marie Curie University, 2nd year - Computer Science [2011-2012]

• • Object-oriented programming

• [2021 - 2022] Louis Falissard. Post-Doc. Methodological developments in the field of machine learning, natural language processing and environmental health sciences. (ANR GePhEx Project; Position Details)

• [2022 - 2025] Amine Ferdjaoui. PhD student. Inference of causal relationships by non-supervised learning applied to Natural Language Processing. Co-supervised with Pr. Mohamed Nadif.

• [2019 - 2022] Louis Geiler. PhD student. Deep learning for churn prediction. Co-supervised with Pr. Mohamed Nadif.

• • [2022] M2 internship -- Constrained co-clustering - Application to biomedical data (ANR GePhEx Project)

  • [2022] M1 internships -- Platforms design for co-clustering approaches parametrisation and results visualisation

  • [2019] M2 internship -- Named Entity recognition for biomedical corpus

  • [2018] M2 internship -- Biomedical image classfication with deep neural networks

  • [2018] M1 project -- Satisfaction evaluation from sentiment analysis based on transfer learning

  • [2017] M1 project -- Word vector similarity based on deep neural network