Statistical Machine Learning and High Dimensional Data Analysis

Enseignants

Hafner Christian;

Langue
d'enseignement

Anglais

Préalables

Concepts et outils équivalents à ceux enseignés dans les UEs

LSTAT2020	Logiciels et programmation statistique de base
LSTAT2120	Linear models
LSTAT2110	Analyse des données

Thèmes abordés

1. Partitioning methods for clustering
2. Statistical approaches for dimension reduction and feature extraction
3. Regularization methods in high dimensions, including linear and nonlinear shrinkage
4. Applications

Contenu

1. Partitioning methods for clustering
   • k-means and variants
   • Nonlinear k-means with kernels
   • Support Vector Machines and other multiple kernel learning machines
   • Spectral clustering
2. Statistical approaches for dimension reduction and feature extraction
   • Factor models and probabilistic PCA
   • Kernels for non-linear PCA
   • Kernels for non-linear ICA
3. Regularization methods in high dimensions, including linear and nonlinear shrinkage
4. Applications

Méthodes d'enseignement

The lectures provide the theoretical material, give many practical examples, and show how to implement the methods in common programming packages. 

Modes d'évaluation
des acquis des étudiants

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

Ressources
en ligne

Transparents, codes R, données

Bibliographie

- 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.

Support de cours

• Transparents

Faculté ou entité
en charge

> LSBA