Lectures related to Singular Value Decomposition

Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.

Covers the decomposition of matrices into triangular blocks and spectral decomposition.

Explains Singular Value Decomposition, focusing on orthogonal vectors and matrix decomposition.

Explores PCA and t-SNE for reducing dimensions and visualizing high-dimensional data effectively.

Covers the Singular Value Decomposition theorem and its application in decomposing matrices.

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.