The course will cover statistical signal processing methods, with application to bioengineering field. The students will become familiar with basic concepts of discrete representation of deterministic and random continuous-time signals, discrete-time random signal analysis, deterministic and random parameter estimation. Various estimation methods will be introduced and compared, such as the method of moments, the maximum likelihood and the linear and non-linear least squares methods. An introduction to Bayesian framework for random parameters and random signals estimation will be provided, with particular emphasis to the problem of linear smoothing, filtering, and prediction. Parametric auto-regressive moving average (ARMA) modeling and identification of discrete-time random signals will be also addressed. Advanced topics in parametric and non-parametric (adaptive and non-adaptive) methods for spectral estimation will be introduced, as well as some basic concepts of time-frequency analysis.