This series of eight lessons addresses the signal-processing problem of estimating properties of a random signal from measurements. Five of the eight lessons concern estimation of frequency domain characteristics such as the power spectrum and coherence. You will learn about the periodogram and why averaging is necessary to obtain acceptable estimates of the power spectral density. You will learn how the averaged periodogram or Welch's method reduces the variance of the periodogram estimator at the expense of resolution loss. In the final two lessons you will learn about maximum likelihood estimation as a general tool for estimating unknown parameters in a random signal.