1. The subjective interpretation of probability 2. Bayesian inference 3. Point estimation 4. Frequentist properties of Bayesian estimators 5. Interval estimation 6. Hypothesis testing 7. Prediction 8. Choice of prior 9. Asymptotic Bayes 10. The linear regression model 11. Basics of random variate generation and posterior simulation 12. Posterior simulation via Markov chain Monte Carlo 13. Hierarchical models 14. Latent variable models 15. Mixture models 16. Bayesian methods for model comparison, selection and big data 17. Univariate time series methods 18. State space and unobserved components models 19. Time series models for volatility 20. Multivariate time series methods