Air Quality Sensing: OpenSense II

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Explores the concept of covariance in differenced measurements and correlation between observations.

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Covers the Pearson correlation, relationship direction, form, strength, and regression model assessment.

Explores cross-correlation, signal detection, Hilbert spaces, orthogonal approximation, and image compression using DCT.

Covers linear regression, correlation analysis, and logistic regression fundamentals, emphasizing the distinction between correlation and causation.

Explores the curse of dimensionality, variable selection methods, coefficient of determination, and limitations of filtering techniques.

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

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