README.md

List of chunks, in the intended order (subject to change):

1. Introduction to the Course
2. A Short Tour of an Inference Problem
3. Review of Probability Theory
4. Generative Models (Model Building I)
5. Bayes Theorem
   Data set: SDSS catalog
   Data set: Cepheids
6. Fitting a Straight Line
7. Approaches to Inference
8. Basic Monte Carlo Sampling (Sampling I)
   Data set: OGLE lightcurve
9. Hierarchical Models (Model Building II)
10. Efficient Monte Carlo Sampling (Sampling II)
11. Model Evaluation and Comparison
12. "Model-free" Models (Model Building III)
    Data set: X-ray CCD Observation
13. Even More Monte Carlo Sampling (Sampling III)
14. Missing Information and Selection Effects (Model Building IV)
15. Introduction to Machine Learning
16. Machine Learning: Linear Regression with Scikit-Learn
17. Machine Learning in Astronomy
18. Detection, Fishing, and Blinding
19. Approximate Methods