Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Orthogonal Projections and Reflections in 2D
Graph Chatbot
Related lectures (45)
Subspaces, Spectra, and Projections
Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.
Linear Algebra: Singular Value Decomposition
Delves into singular value decomposition and its applications in linear algebra.
Diagonalization of Linear Transformations
Covers the diagonalization of linear transformations in R^3, exploring properties and examples.
Projections and Applications
Explores projections in R³, matrix properties, and trace concepts with practical examples.
Spectral Theorem Recap
Revisits the spectral theorem for symmetric matrices, emphasizing orthogonally diagonalizable properties and its equivalence with symmetric bilinear forms.
Translations and Homotheties
Covers translations, homotheties, and their analytical expressions, emphasizing stability by composition.
Orthogonal Projections and Reflections
Covers the analytical expression of orthogonal projections and reflections in 2D space.
Matrix Reduction: Part 1
Covers the reduction of a linear transformation in a 2-dimensional space to find a simpler matrix representation.
Diagonalization of Matrices
Log in to Mediaspace to watch this video
Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.
Linear Applications and Eigenvectors
Log in to Mediaspace to watch this video
Covers linear applications, diagonalizable matrices, eigenvectors, and orthogonal subspaces in R^n.
Characteristic Polynomials and Similar Matrices
Log in to Mediaspace to watch this video
Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.
Projections and Symmetries
Log in to Mediaspace to watch this video
Explores projections on lines and symmetries in 2D space, emphasizing fixed points and symmetric matrices.
Matrix Diagonalization: Spectral Theorem
Log in to Mediaspace to watch this video
Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.
Symmetric Matrices: Diagonalization
Log in to Mediaspace to watch this video
Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.
Spectral Decomposition of Symmetric Matrices
Log in to Mediaspace to watch this video
Explores the spectral decomposition of symmetric matrices, including diagonalization and orthogonal basis change matrices.
Symmetric Matrices: Properties and Decomposition
Log in to Mediaspace to watch this video
Covers examples of symmetric matrices and their properties, including eigenvectors and eigenvalues.
Orthogonal Matrices & Spectral Decomposition
Log in to Mediaspace to watch this video
Covers the process of finding orthogonal bases and spectral decomposition of symmetric matrices.
Symmetric Matrices and Eigenvectors
Log in to Mediaspace to watch this video
Covers the concept of symmetric matrices, orthogonal bases, and eigenvectors.
Diagonalization of Symmetric Matrices
Log in to Mediaspace to watch this video
Covers the diagonalization of symmetric matrices, the spectral theorem, and the use of spectral decomposition.
Decomposition Spectral: Symmetric Matrices
Log in to Mediaspace to watch this video
Covers the decomposition of symmetric matrices into eigenvalues and eigenvectors.
Previous
Page 1 of 3
Next