Lecture Topics

  1. Fundimental properties of probability .
  2. Random Variables.
  3. Classic probability distributions.
  4. Bayesian parameter estimation and model selection.
  5. Frequentist hypotheses testing.
  6. Fisher matrices.
  7. Non-parametric tests.
  8. Random Fields.
  9. Image reconstruction and map making.
  10. Numerical methods for the Bayesian Inference problem.
  11. supervised and unsupervised machine learning methods for classification.

Lecture Notes

The current version of the notes is available here which has a table of contents. They will be regularly updated.