Eigenvalues, Eigenvectors and Diagonalization of Matrices

This course provides a clear and structured exploration of eigenvalues, eigenvectors, and diagonalization, focusing on both theory and real-world applications. You’ll learn how to compute eigenvalues and eigenvectors, understand their geometric significance, and apply diagonalization to simplify complex matrix operations. Topics include linear transformations, dynamical systems, and applications in physics, engineering, and machine learning. The course is designed for students, engineers, and data scientists seeking a strong foundation in matrix methods. By the end, you'll confidently apply these concepts in problem-solving and computational modeling.

3

$ 10.00

One-time payment

Enrolment valid for 12 months

Course Chapters

1
Introduction

Welcome and course outline. Review of elementary matrix concepts.

2
Eigenvalues and Eigenvectors

Meaning, operations and properties of eigenvalues and eigenvectors of matrices.

3
Diagonalization of Matrices

Diagonalization of matrices; evaluating polynomials and transcendentals of matrices.

4
Symmetric Matrices

Diagonalization of symmetric matrices - orthogonal diagonalizing matrix, the Gram-Schmidt orthogonalization procedure, and related concepts.

5
Quadratic and Canonical Forms

Quadratic and canonical forms; transformations using symmetric matrices and orthogonal diagonalizing matrices.