EE-312: Matrix analysis

Lectures in this course (11)

Covers the application of linear algebra in quantum mechanics, focusing on states, superposition, measurements, and entangled states.

Explores auto-encoders, covering invertibility conditions, projections, and generalized inverses in linear algebra.

Covers the Moore-Penrose pseudoinverse and its properties for arbitrary matrices.

Explores the application of matrices and eigendecompositions in networks.

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Explores the power of linear systems in control theory and data science.

Explores linear least squares, normal equations, and the importance of linear regression in minimizing errors.

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

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

Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.

Covers the concept of fundamental spaces in linear algebra and how to calculate them.

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