Back to Linear Algebra: Orthogonality and Diagonalization
Johns Hopkins University

Linear Algebra: Orthogonality and Diagonalization

This is the third and final course in the Linear Algebra Specialization that focuses on the theory and computations that arise from working with orthogonal vectors. This includes the study of orthogonal transformation, orthogonal bases, and orthogonal transformations. The course culminates in the theory of symmetric matrices, linking the algebraic properties with their corresponding geometric equivalences. These matrices arise more often in applications than any other class of matrices. The theory, skills and techniques learned in this course have applications to AI and machine learning. In these popular fields, often the driving engine behind the systems that are interpreting, training, and using external data is exactly the matrix analysis arising from the content in this course. Successful completion of this specialization will prepare students to take advanced courses in data science, AI, and mathematics.

Status: Geometry
Status: Applied Mathematics
IntermediateCourse9 hours

Featured reviews

HK

Reviewed Dec 8, 2024

Teach good. It explore some of my blind areas about diagonalization, eigen and orthogonal, repeated roots concern, etc.

MD

Reviewed Nov 4, 2024

It is great, the guy on the videos knows a lot, its a pity he writes so fast :))

All reviews

Showing: 13 of 13

Kunal
5.0
Reviewed Oct 26, 2025
Will
5.0
Reviewed Jul 13, 2025
William
5.0
Reviewed Nov 12, 2024
Afsin
5.0
Reviewed Feb 19, 2024
Hung
5.0
Reviewed Dec 8, 2024
Maciej
5.0
Reviewed Nov 5, 2024
Chadwick
5.0
Reviewed Mar 31, 2025
Kenneth
5.0
Reviewed Jan 22, 2025
GOLKONDA
5.0
Reviewed Oct 31, 2024
Taras
5.0
Reviewed Jul 3, 2026
Jyun-Hao
5.0
Reviewed Oct 31, 2024
Anastasia
4.0
Reviewed Apr 30, 2024
Ben
3.0
Reviewed Jul 30, 2026