Lectures related to General Conics and Homogeneous Coordinates

Explores spectral decomposition of symmetric matrices and Singular Value Decomposition (SVD) for matrix decomposition.

Covers the basics of linear algebra, including matrix operations and singular value decomposition.

Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.

Covers the properties and equations of conic sections, including the ellipse, hyperbola, and parabola.

Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.

Covers the definition of quadratic forms in R^n with examples in R^2 and R^3.

Covers linear systems, diagonal and triangular matrices, and LU factorization.