Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Practice
Publications
Startups
Units
Show all results for
Home
Lecture
Eigenvalues and Eigenvectors: Definitions, Examples
Graph Chatbot
Related lectures (26)
Previous
Page 2 of 3
Next
Eigenvalues and Eigenvectors
Covers eigenvalues, eigenvectors, and characteristic polynomials in matrix transformations.
Linear Algebra: Reduction of Linear Application
Covers the reduction of a linear application and finding corresponding reduced forms and bases.
Diagonalization of Linear Transformations
Covers the diagonalization of linear transformations in R^3, exploring properties and examples.
Diagonalization of Matrices: Theory and Examples
Covers the theory and examples of diagonalizing matrices, focusing on eigenvalues, eigenvectors, and linear independence.
Linear Algebra: Fundamentals
Introduces fundamental concepts in linear algebra, such as vector spaces and eigenvalues.
Eigenvalues and Diagonalization
Covers eigenvalues, eigenvectors, and diagonalization of matrices.
Linear Applications and Eigenvectors
Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.
Matrix Similarity and Diagonalization
Explores matrix similarity, diagonalization, characteristic polynomials, eigenvalues, and eigenvectors in linear algebra.
Linear Algebra: Eigenvalues and Eigenvectors
Explores eigenvalues, eigenvectors, diagonalization, and spectral theorem in linear algebra.
Orthogonal Projection: Spectral Decomposition
Covers orthogonal projection, spectral decomposition, Gram-Schmidt process, and matrix factorization.