1. Basics of probability and linear algebra
  2. ML Basics, evaluation of ML models
  3. What is different in healthcare? Health data modalities, imbalance, rare events, stakeholders, etc.
  4. Intro to pandas, sklearn, pytorch
  5. Classification methods for healthcare
    • Diagnosis, prognosis, stratification/staging, segmentation
    • SVM, random forests, xgboost
  6. Unsupervised methods for healthcare
    • PCA, ICA, Tensor decomposition
    • Clustering
  7. Graphical models for healthcare
    • Bayesian networks
    • Hidden Markov models
  8. Causality
    • Causal discovery
    • Causal inference
  9. Deep learning for healthcare
    • CNNs
    • Recurrent networks
    • Autoencoders
    • Self-supervised learning
    • Generative models
    • Large language models
  10. Federated learning and privacy
  11. Trustworthy ML for healthcare
  12. Explainability
  13. Robustness
  14. Fairness
  15. Safety
  16. Ethics of AI in Healthcare