Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.
Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Introduces the fundamentals of noise in electronics, covering origins, signal types, power characteristics, and noise sources.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.
