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: Algebra
Status: Applied Mathematics
IntermediateCourse9 hours

Featured reviews

MD

5.0Reviewed Nov 4, 2024

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

CC

5.0Reviewed Mar 30, 2025

Well taught, clearly explained, thorough and helpful examples throughout

HK

5.0Reviewed Dec 8, 2024

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

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Kunal Kumar
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Reviewed Oct 26, 2025
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Anastasia Meyer
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Reviewed Apr 30, 2024