Covers the concept of time, discrete time practicality, sampling theorem, digital storage, transmission of signals, and key ideas of digital signal processing.
Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.
Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.
