Signals & Systems I: Cross-Correlation and Convolution

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Covers signal generation, statistical relations, stationarity, white noise, and orthogonality in stochastic processes.

Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.

Covers scalar products, orthogonal vectors, norms, and projections in vector spaces, emphasizing orthonormal families of vectors.

Explores orthogonality, Gram-Schmidt process, dot products, and solution minimization in systems.

Explores the fundamentals of time series analysis, including stationarity, linear processes, forecasting, and practical aspects.

Explores Hilbert spaces, orthonormal systems, and the Bessel inequality, emphasizing their properties and significance.

Explores dependence concepts, copulas, correlation fallacies, and rank correlations in statistics.

Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.

Covers the main properties of time-frequency domain signals and their limitations.

Introduces function spaces and Hilbert spaces, discussing inner product spaces and the importance of completeness in Hilbert spaces.