Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Covers the calculation of Fourier coefficients for periodic complex signals and the concept of a complete orthonormal system.
Covers the key concepts of PCA, including reducing data dimensionality and extracting features, with practical exercises.
Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
