Supervised learning in R (6-8 hours): Preprocessing, cross-validation, lasso, decision trees, random forest, xgboost, and superlearner ensembles.

Deep learning in R (6-8 hours): Deep learning with Keras - building & training deep networks, image classification, transfer learning, text analysis, and visualization

Unsupervised learning in R (6-8 hours): Clustering (Hdbscan, LCA, Hopach), dimensionality reduction (GLRM, UMAP), and anomaly detection (isolation forests)

Guide to SuperLearner (4-6 hours): Basic ensembles, hyperparameter tuning, nested cross-validation, parallelization, diagnostics, feature selection, and loss customization.

Causal inference with targeted learning (6-8 hours): causal diagrams, regression with SuperLearner, inverse probability of treatment weighting, targeted maximum likelihood estimation, effect modification, causal variable importance, exposure mixture modeling

Feature selection in R (6-8 hours): permutation importance, adaptive elastic net, relief family (relief-f, STIR, multisurf), joint mutual information, and knockoffs