teaching
Materials for courses I teach
Below is a collection of teaching materials covering statistical and machine learning techniques with both practical applications and detailed mathematical derivations. Many include complete implementations from scratch in R.
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Data Integration using random forest:
Learn how to use random forests to integrate multi-omics datasets, extract proximity measures, and perform downstream analysis like clustering and dimensionality reduction. -
Data Integration using mixOmics:
Explore supervised and unsupervised data integration techniques using themixOmics
package, including PLS, sPLS, and DIABLO, with full implementation guidance. -
Data Integration using MOFA:
Understand the theory behind Multi-Omics Factor Analysis (MOFA) and how to use it for uncovering shared and specific latent factors across data types, including implementation in R from scratch. -
Data Integration using mixKernel:
A hands-on and theoretical introduction to kernel-based integration approaches, including customized kernels and combining them with PCA. -
PCA basics:
Covers Principal Component Analysis from first principles, including variance maximization, SVD connection, visualization, and full R implementation. -
Random forest basics:
A conceptual and mathematical breakdown of random forests, how trees are built, how splits are selected, and how to interpret feature importance. -
Mixed models:
Detailed explanation of linear mixed models including GLS derivations from scratch, variance components, ICC, REML vs ML, and how to implement models usinglme4
and interpret random effects. -
t-SNE and UMAP:
Dimensionality reduction methods with detailed mathematical steps from pairwise distances to probabilities, including R implementations of both t-SNE and UMAP from scratch. -
Introduction to Self-Organizing Maps:
Understand the theory behind SOMs and how to implement and visualize them in R. Includes clustering and interpretation strategies for omics data. -
Independent Component Analysis:
Complete mathematical derivation of ICA including contrast functions, whitening, optimization, and implementation of FastICA in R.