Statistical Signal and Data Processing through Applications is the follow-up to Bachelor courses on signal processing, such as "Signal Processing for Communications", or the Master course “Signal Processing Foundations” where the basics of signal processing were introduced. Building up on the basic concepts of sampling, filtering and Fourier transforms, we address spectral estimation, signal detection, classification, and adaptive filtering, with an application oriented approach: We first introduce relevant modern applications, such as neurobiological data analysis, spread spectrum wireless communications, echo cancellation, and the discuss appropriate statistical methods and tools to tackle related problems. The idea is to develop a "toolbox" of signal processing methods and learn how to use it for solving interesting problems arising in actracting applications.
- Professor: Andrea Ridolfi
- Teacher: Michalina Wanda Pacholska
