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
Project using a real data set (40%) , and an oral exam (60%)
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
