Teaching

Use Random Forests to integrate multi-omics datasets, extract proximity measures, and perform downstream analyses including clustering and dimensionality reduction.

Supervised and unsupervised multi-omics integration with mixOmics, including PLS, sPLS, and DIABLO.

Multi-Omics Factor Analysis from its underlying latent-factor model through practical implementation and interpretation.

Kernel-based data integration, custom similarity measures, and combining kernel representations with dimensionality reduction.

Principal Component Analysis from first principles: geometry, variance maximization, eigenvectors, SVD, visualization, and implementation in R.

A conceptual and mathematical introduction to decision trees and Random Forests, including splitting, bagging, feature randomness, and variable importance.

Linear mixed models from GLS and variance components through ICC, REML versus ML, random effects, and practical modelling with lme4.

Nonlinear dimensionality reduction from pairwise distances and probability distributions through detailed t-SNE and UMAP implementations.

The theory and practical use of Self-Organizing Maps for high-dimensional data, including training, visualization, clustering, and interpretation.

ICA from latent-variable intuition through whitening, non-Gaussianity, FastICA optimization, mathematical derivation, and implementation in R.

XGBoost from intuition to mathematics: sequential boosting, tree-based error correction, objective functions, regularization, and practical implementation in R.