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 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).
- 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)).