Lectures related to Experimental Designs: Aliases and Model Decomposition

Covers the concept of Singular Value Decomposition (SVD) for compressing information in matrices and images.

Covers the QR factorization method applied to solving a system of linear equations in the least squares sense.

Covers the analytical expression of orthogonal projections and reflections in 2D space.

Explores multicorrelation sequences, primes, and their intricate connections in number theory and ergodic theory.

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

Explores Singular Value Decomposition, low-rank approximation, fundamental subspaces, and matrix norms.

Covers the Singular Value Decomposition (SVD) of a matrix and its applications.

Covers unsupervised learning with a focus on Principal Component Analysis and the Singular Value Decomposition.

Covers the decomposition of a matrix into its eigenvalues and eigenvectors, the orthogonality of eigenvectors, and the normalization of vectors.

Explores latent factor analysis for movie genre classification based on male versus female leads.

Explores architectural transformations optimizing area, time, and product in digital system design.

Explores symmetrical bilinear forms, the Pythagorean theorem, inequalities, and orthogonal matrices.

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

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

Covers the fundamentals of Singular Value Decomposition, including properties, applications, and error measurement.

Covers the QR factorization of a matrix A into Q and R.

Covers Singular Value Decomposition, focusing on its application in image compression and data representation.

Introduces QR factorization for matrix decomposition, emphasizing its importance in various applications and the implications of a well-chosen model.

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

Explores spectral and singular value decompositions of matrices.