Course 4
COURSE 4 · ML & HIGH-DIM
Schedule
Every lab, every deck, every R script for Course 4.
| Week | # | Topic | Article | Slides | Script |
|---|---|---|---|---|---|
| 1 | 1 | Cross-validation, nested CV, bootstrap | lab | slides | R |
| 1 | 2 | Regularisation: ridge, lasso, elastic net | lab | slides | R |
| 1 | 3 | PCA, FA, CCA, LDA | lab | slides | R |
| 1 | 4 | Clustering: k-means, hierarchical, model-based | lab | slides | R |
| 1 | 5 | UMAP and t-SNE | lab | slides | R |
| 2 | 6 | Trees, random forests, boosting | lab | slides | R |
| 2 | 7 | Interpretability and SHAP | lab | slides | R |
| 2 | 8 | Tabular neural networks with torch | lab | slides | R |
| 2 | 9 | Imaging and sequence models intro | lab | slides | R |
| 2 | 10 | tidymodels pipelines | lab | slides | R |
| 3 | 11 | Bayesian thinking; α vs decision error | lab | slides | R |
| 3 | 12 | brms / Stan, LOO, hierarchical | lab | slides | R |
| 3 | 13 | Biomarker statistics | lab | slides | R |
| 3 | 14 | Survival ML | lab | slides | R |
| 3 | 15 | Time-dependent Brier, IPA, external validation | lab | slides | R |
| 4 | 16 | RNA-seq with DESeq2/edgeR | lab | slides | R |
| 4 | 17 | Enrichment analysis | lab | slides | R |
| 4 | 18 | scRNA-seq with Seurat | lab | slides | R |
| 4 | 19 | FDR, knockoffs, replication crisis | lab | slides | R |
| 4 | 20 | TRIPOD-AI, fairness, reproducibility at scale | lab | slides | R |