Selected videos:

• Data depth approach for anomaly detection (9 min):
  https://www.youtube.com/watch?v=5Wxrg1yj3wc
• Explainability of artificial intelligence (3 min):
  https://imtech.wp.imt.fr/tag/pavlo-mozharovskyi/
• Machine learning for anomaly detection, part 1 (1 hour 16 min):
  https://www.youtube.com/watch?v=Y4DOQnv76cg
• Machine learning for anomaly detection, part 2 (1 hour 20 min):
  https://www.youtube.com/watch?v=ZhVqGqXt34o

Selected teaching materials:

Machine learning (course):

• Brief introduction to machine learning
  [lecture slides] [handout version] [tutorial].
• Classification tree, bagging, and random forest
  [lecture slides] [handout version] [tutorial].
• Boosting algorithms
  [lecture slides] [handout version] [tutorial].
• Support vector machines
  [lecture slides] [handout version] [tutorial].
• Perceptron, neural network, and the back-propagation algorithm
  [lecture slides] [handout version] [tutorial R] [tutorial Python].

Non-supervised learning (Data Science course for BPCE):

• Statistical basis
  [lecture slides] [R tutorial] [Python tutorial].
• Clustering
  [lecture slides] [Python tutorial].
• Anomaly detection
  [lecture slides (1)] [lecture slides (2)] [R tutorial (1)] [Python tutorial (1)] [R tutorial (2)] [Python tutorial (2)].

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:

• Play-around on anomaly detection [R notebook].
• Hurricanes [R notebook].
• Brain imaging [R notebook].
• Cars [Python notebook].
• Airbus data [Python notebook].

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 2023-2024:

• Statistics (2nd year, MDI220).
• Data Science via Quantum Entanglement (1st year, PM101).
• Tail Events Analysis : Robustness, Outliers and Models for Extreme Values (Master Data Science, DATA918).
• Advanced Machine Learning (Specialised Master Big Data, MDI341).

Courses taught in scholar year 2022-2023:

• Statistics (2nd year, MDI220).
• 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 BPCE).

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)).