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
Non-Negative Definite Matrices and Covariance Matrices
Graph Chatbot
Related lectures (46)
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.
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.
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.
Principal Component Analysis: Properties and Applications
Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.
Multicollinearity: Dangers and Remedies
Explores the dangers of multicollinearity in linear models and discusses diagnostic methods and remedies.
Linear Models: Least Squares
Explores linear models, least squares, Gaussian vectors, and model selection methods.
Classification of Quadratic Forms
MOOC: Linear Algebra (Part 3)
Explores the classification of quadratic forms based on eigenvalues and orthogonal diagonalization of symmetric matrices.
Multivariate Statistics: Introduction and Methods
Introduces multivariate statistics, focusing on uncovering associations between components in data in vector form.
Subspaces, Spectra, and Projections
Explores subspaces, spectra, and projections in linear algebra, including symmetric matrices and orthogonal projections.
Market Response Functions
Explores market response functions, flash crashes, correlation estimation, and noise filtering in finance.
Linear Models: Ridge, OLS and LASSO
Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.
Quadratic Forms in IR³
MOOC: Linear Algebra (Part 3)
Explores quadratic forms in IR³, matrix properties, diagonalization, and positive definite matrices.
Principal Component Analysis: Dimension Reduction
Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.
Sylvester's Inertia Theorem
Explores Sylvester's Inertia Theorem, relating eigenvalues to diagonal entries in symmetric matrices.
Symmetric Matrices and Quadratic Forms
Log in to Mediaspace to watch this video
Explores symmetric matrices, diagonalization, and quadratic forms properties.
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.
Symmetric Matrices and Quadratic Forms
Log in to Mediaspace to watch this video
Explores symmetric matrices, quadratic forms, diagonalization, and definiteness with examples and calculations.
Previous
Page 1 of 3
Next
