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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.

状态:Numerical Analysis
状态:Applied Mathematics
中级课程小时

精选评论

CC

5.0评论日期:Mar 30, 2025

Well taught, clearly explained, thorough and helpful examples throughout

HK

5.0评论日期:Dec 8, 2024

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

MD

5.0评论日期:Nov 4, 2024

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

所有审阅

显示:11/11

Kunal Kumar
5.0
评论日期:Oct 26, 2025
Will Perlichek
5.0
评论日期:Jul 13, 2025
William Stanton
5.0
评论日期:Nov 12, 2024
Afsin Yilmaz
5.0
评论日期:Feb 19, 2024
Hung Wai Kay
5.0
评论日期:Dec 8, 2024
Maciej Durczok
5.0
评论日期:Nov 5, 2024
Chadwick
5.0
评论日期:Mar 31, 2025
Kenneth Byrne
5.0
评论日期:Jan 22, 2025
GOLKONDA RAJESH
5.0
评论日期:Oct 31, 2024
Jyun-Hao Chen
5.0
评论日期:Oct 30, 2024
Anastasia Meyer
4.0
评论日期:Apr 30, 2024