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Lecture notes:
1: Introduction to unix and python
2: More python; FITS
3: Good coding style; data; noise
4: MCs; uncerts; graphs; minimization
5: Minimization; splines; regridding
6: Model fitting; hypothesis testing
7: Chi^2 and all that
8: Source detection
9: Source catalog issues
10: Catalog biases; cross matching
11: Radio astronomy (out of order)
12: Complex numbers and the Fourier transform.
13: More radio astronomy
14: Radio interferometry
15: Bayesian variability analysis; the DFT
16: More interferometry
17: Weighting of visibilities
18: The CLEAN algorithm
19: MEM; calibration; spectroscopy
20: High Energy Astronomy
21: HEA, attitude, instrumentation
22: XMM-Newton
23: XMM-Newton continued
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