Teaching materials

Selected videos:

Selected teaching materials:

Machine learning (course):
Multivariate and functional anomaly detection (tutorial):

for the chair DSAIDIS, given on the 6th and 13th of April 2022:

  • Lecture slides – Part I: Multivariate anomaly detection [pdf].
  • Lecture slides – Part II: Functional anomaly detection [pdf].

Notebooks:

Data sets:

  • Data set on anomaly detection for cars. [csv file].
  • Data set from Airbus. [csv file].
  • Historical hurricane data. [txt file].
  • Anatomical brain volume data. [nii file].
  • Left brain fiber’s bundle. [txt file].
  • Right brain fiber’s bundle. [txt file].

Supplementary scripts:

  • Routines for data depth calculation. [Python code].
  • Implementation of the functional isolation forest. [Python code].
  • Routines for curves’ parametrization. [R code].
  • Routines for input of brain imaging data. [R code].

Courses taught at Télécom Paris:

Courses taught in scholar year 2021-2022:

  • Statistics (2nd year, MDI220).
  • Statistical Learning and Data Mining (Specialised Master Big Data, MDI343).
  • Statistical Learning (M2MO Random Modelling, Finance and Data Science).
  • Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).
  • Advanced Machine Learning (Specialised Master Big Data, MDI341).
  • Non-supervised learning (Special course for Natixis).

Courses taught in scholar year 2020-2021:

  • Statistics (2nd year, MDI220).
  • Non-supervised learning (Special course for Natixis).
  • Statistical Learning and Data Mining (Specialised Master Big Data, MDI343).
  • Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).

Courses taught in scholar year 2019-2020:

  • Statistical Module (Master Artificial Intelligence, MDI721).
  • Statistical Learning (Master Artificial Intelligence, IA710).
  • Statistical Learning and Data Mining (Specialised Master Big Data, MDI343).
  • Statistics: Linear Models (2nd year, SD-TSIA204).
  • Machine Learning for Multimedia (Master Multimedia, MN915).
  • Advanced Machine Learning (Specialised Master Big Data, MDI341).
  • Machine Learning (2nd year, SD-TSIA210).
  • Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).

Courses taught in scholar year 2018-2019:

  • Statistics (2nd year, MDI220).
  • Linear Models (Specialised Master Big Data, MDI720).
  • Statistical Learning and Data Mining (Specialised Master Big Data, MDI343/MDI724).
  • Statistics: Linear Models (2nd year, SD-TSIA204).
  • Machine Learning for Multimedia (Master Multimedia, MN915).
  • Advanced Machine Learning (Specialised Master Big Data, MDI341/MDI732).
  • Machine Learning (2nd year, SD-TSIA210 (CrD)).