Lectures related to Multivariate Statistics: Wishart and Hotelling T²

Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Explains the construction of U, verification of results, and interpretation of SVD in matrix decomposition.

Introduces inferential statistics, covering sampling, central tendency, dispersion, histograms, z-scores, and the normal distribution.

Explores Bourgain's theorem on sparsest cut in graphs, emphasizing semimetrics and cut optimization.

Explores the equivalence between different properties of linear transformations represented by matrices and various matrix operations.