Orthogonal Projections: Gram-Schmidt Method

Related lectures (25)

Page 2 of 3

Covers orthogonality, scalar products, orthogonal bases, and vector projection in detail.

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

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

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

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

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

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

Covers the theorems related to orthogonal projection and orthonormal bases.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Explores spectral and singular value decompositions of matrices.