Spectral Theorem: Eigenvalues and Eigenvectors

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (29)

Page 3 of 3

Covers eigenvalues, eigenvectors, and the Fibonacci sequence, exploring their mathematical properties and practical applications.

Diagonalization of Matrices and Least Squares

Covers diagonalization of matrices, eigenvectors, linear maps, and least squares method.

Linear Algebra: Canonical Basis

Explores the canonical basis in linear algebra, focusing on matrix representation, diagonalizability, and characteristic polynomials.

Exponential of Operators and Matrices

Covers the exponential of operators and matrices, commutation properties, Jordan normal form, and linear algebra concepts related to linear operators and eigenvalue problems.

Linear Algebra: Singular Value Decomposition

Delves into singular value decomposition and its applications in linear algebra.

Eigenvalues and Eigenvectors: Understanding Matrices

Explores eigenvalues and eigenvectors in matrices through examples and calculations.

Best approximative subspace

Explores finding the minimal solution of a problem using orthogonal bases and factorization, emphasizing uniqueness and practical examples.

Diagonalization of Matrices: Eigenvectors and Eigenvalues

Covers the concept of diagonalization of matrices through the study of eigenvectors and eigenvalues.

Linear Regression: Absence or Presence of Covariates

Explores linear regression with and without covariates, covering models captured by independent distributions and tools like subspaces and orthogonal projections.