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Explores cross-correlation, convolution, signal approximation, orthonormal functions, and orthogonal approximation theorem in signals and systems.

Covers stylized facts in finance and reproducible research in scientific computing.

Explains coherence transfer techniques in multi-dimensional NMR spectroscopy.

Explores copulas in multivariate statistics, covering properties, fallacies, and applications in modeling dependence structures.

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

Covers variance reduction techniques in stochastic simulation to improve accuracy and efficiency.

Explores real estate data analysis through descriptive statistics, correlation, regression, and forecasting methods.

Explores causality, correlation, and spurious correlations in machine learning, emphasizing bias mitigation and invariance across environments.

Explores variance, covariance, and practical applications in statistics and probability.

Covers the key concepts of Principal Component Analysis (PCA) and its practical applications in data dimensionality reduction and feature extraction.