Orthogonality

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Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Covers penalization in ridge regression, emphasizing the trade-off between bias and variance in regression models.

Explains matrix operations, orthogonality, and least squares method in solving linear equations.

Covers Lyapunov exponents, chaos measurement, and perturbation analysis in dynamical systems.

Explores orthogonality, dot product properties, vector norms, and angle definitions in vector spaces.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Covers entropic notions in quantum sources, Shannon entropy, Von Neumann entropy, and source coding.

Explores orthogonal bases in vector spaces, explaining unique vector representations and spectral decomposition.

Explains orthogonal families, bases, and projections in vector spaces.

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