Machine Learning: Foundations

Available Data Sets:

  1. Iris: Data Information.
  2. australian: Data Information
  3. heart: Data Information
  4. diabetes: Data Information

More datasets: UCI Machine Learning database repository

Homeworks:

FINAL PROJECT

Scribe notes:

  1. Introduction.
  2. Bayesian Inference.
  3. Nearest Neighbors
  4. PAC model and Occam Razor
  5. Boosting and Experts (the paper)
  6. Decision Trees - Splitting Criteria
  7. VC dimension (definition and impossibility result)
  8. VC dimension (sample bound)
  9. Model Selection
  10. Information Theory
  11. Decision tree pruning