Theoretical aspects:

Lecture 1:
	✴	auto-covariance and auto-correlation function (HM 6.1.1 -->)
	✴	convolution theorem to explain relation between timeseries, autocovariance and spectrum (Manu’s notes)
	✴	degrees of freedom (HM --> 6.1.6)

Lecture 2:
	✴	FFT in MATLAB
	✴	aliasing of signals, NQUIST frequency, trends (unresolved low-frequency components) (HM 6.2.2)
	✴	Computing and plotting power spectra with FFT (HM 6.2.4)
	✴	Window tapering and spectral smoothing/averaging (HM 6.2.5)

Lecture 3:
	✴	example of testing the significance of a correlation (e.g. two white noise, for auto-correlated timeseries two red noise)
	✴	red-noise as a default model or null-hypothesis for a time process,
	✴	how to use red-noise to assess the significance of spectral peaks and, F-test (HM 6.2.6)