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Advanced Linear Algebra, Lecture 2.4: The four subspaces
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Advanced Linear Algebra - Advanced Linear Algebra, Lecture 2.4: The four subspaces

Unlock the Power of Vector Spaces: Master Advanced Linear Algebra with Professor Macauley. Dive deep into theory and applications, from eigenvectors to spectral theorems. Enhance your mathematical prowess and transform complex problems into elegant solutions.

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43 learners

What you'll learn

Understand key concepts of vector spaces, including spanning, independence, and bases.
Analyze the role of eigenvalues, eigenvectors, and the spectral theorem in linear mappings.
Apply the Gram-Schmidt process and orthogonal projection in various contexts.
Evaluate the properties and applications of quadratic forms and spectral resolutions.

This course includes

  • 25.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

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Advanced Linear Algebra, Lecture 2.4: The four subspaces In this lecture, we will give a brief review of some key concepts from undergraduate linear algebra, in preparation for upcoming lectures were we abstract concepts such as the transpose, and learn how to encode a general linear map with a matrix. Though this is not necessary, it will help give some context for objects and results which may otherwise seem abstract and unfounded. Every matrix has four fundamental subspaces: the column space, the row space, the nullspace, and the left null space. In this lecture, we review these and show how they fit together. We also see four ways to multiply matrices: (i) rows by columns, (ii) by columns, (iii) by rows, and (iv) columns by rows. Finally, we do an example of Gaussian elimination to see how this relates to the four subspaces. Course webpage: http://www.math.clemson.edu/~macaule/math8530-online.html

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