Teacher(s)
Language
English
Prerequisites
Concepts and tools equivalent to those taught in teaching units
| LSTAT2020 | Logiciels et programmation statistique de base |
| LSTAT2120 | Linear models |
| LSTAT2110 | Analyse des données |
Main themes
- Partitioning methods for clustering
- Statistical approaches for dimension reduction and feature extraction
- Regularization methods in high dimensions, including linear and nonlinear shrinkage
- Applications
Content
- Partitioning methods for clustering
- k-means and variants
- Nonlinear k-means with kernels
- Support Vector Machines and other multiple kernel learning machines
- Spectral clustering
- Statistical approaches for dimension reduction and feature extraction
- Factor models and probabilistic PCA
- Kernels for non-linear PCA
- Kernels for non-linear ICA
- Regularization methods in high dimensions, including linear and nonlinear shrinkage
- Applications
Teaching methods
The lectures provide the theoretical material, give many practical examples, and show how to implement the methods in common programming packages.
Evaluation methods
Oral exam
Online resources
Slides, R codes and data
Bibliography
- Everitt, B. and Hothorn, T. (2011). An Introduction to Applied
Multivariate Analysis with R, Springer Verlag.
- Härdle, W. and Simar, L. (2015). Applied Multivariate Statistical
Analysis, Springer Verlag.
- Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of
Statistical Learning, Springer Verlag.
- Izenman, A.J. (2008) Modern multivariate statistical techniques, Springer
- James, Witten, Hastie, Tibshirani (2013) An Introduction to statistical
learning with applications in R, Springer
- Koch, I. (2014) Analysis of multivariate and high-dimensional data,
Cambridge
- Ledolter, J. (2013), Data Mining and Business Analytics with R, Wiley
- Zaki, M.J. and Meira, W. (2020) Data Mining and Machine Learning,
fundamental concepts and algorithms, 2nd ed., Cambridge.
Multivariate Analysis with R, Springer Verlag.
- Härdle, W. and Simar, L. (2015). Applied Multivariate Statistical
Analysis, Springer Verlag.
- Hastie, T., Tibshirani, R. and Friedman, J. (2009). The Elements of
Statistical Learning, Springer Verlag.
- Izenman, A.J. (2008) Modern multivariate statistical techniques, Springer
- James, Witten, Hastie, Tibshirani (2013) An Introduction to statistical
learning with applications in R, Springer
- Koch, I. (2014) Analysis of multivariate and high-dimensional data,
Cambridge
- Ledolter, J. (2013), Data Mining and Business Analytics with R, Wiley
- Zaki, M.J. and Meira, W. (2020) Data Mining and Machine Learning,
fundamental concepts and algorithms, 2nd ed., Cambridge.
Teaching materials
- Transparents
Faculty or entity
