Linear Algebra Basics

Related lectures (27)

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Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.

Covers the concept of basis, linear transformations, matrices, inverses, determinants, and bijective transformations.

Explores normal equations, pseudo-solutions, unique solutions, and symmetric matrices in linear algebra.

Covers linear algebra basics, including matrices, change of basis, and invertible matrices.

Explores vector subspaces in R4, symmetric matrices, basis vectors, and canonical forms.

Covers matrix notation, linear equations, vector spaces, and applications of least squares.

Explores eigenvalues and eigenvectors in 3D linear algebra, covering characteristic polynomials, stability under transformations, and real roots.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

Covers linear independence, bases, and coordinate systems with examples and theorems.

Covers fundamental linear algebra concepts like vector spaces and eigenvalues.