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
Non-Negative Definite Matrices and Covariance Matrices
Graph Chatbot
Related lectures (30)
Previous
Page 1 of 3
Next
Principal Components: Properties & Applications
Explores principal components, covariance, correlation, choice, and applications in data analysis.
Spectral Theorem Recap
Revisits the spectral theorem for symmetric matrices, emphasizing orthogonally diagonalizable properties and its equivalence with symmetric bilinear forms.
Symmetric Matrices and Quadratic Forms
Explores symmetric matrices, diagonalization, and quadratic forms properties.
Matrix Diagonalization: Spectral Theorem
Covers the process of diagonalizing matrices, focusing on symmetric matrices and the spectral theorem.
Symmetric Matrices: Diagonalization
Explores symmetric matrices, their diagonalization, and properties like eigenvalues and eigenvectors.
Multivariate Statistics: Wishart and Hotelling T²
Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.
Symmetric Matrices and Quadratic Forms
Explores symmetric matrices, quadratic forms, diagonalization, and definiteness with examples and calculations.
Matrices and Quadratic Forms: Key Concepts in Linear Algebra
Provides an overview of symmetric matrices, quadratic forms, and their applications in linear algebra and analysis.
Canonical Correlation Analysis: Overview
Covers Canonical Correlation Analysis, a method to find relationships between two sets of variables.
Principal Component Analysis: Theory and Applications
Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.
