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University of Minnesota

Matrix Methods

University of Minnesota via Coursera


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Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.


  • Matrices as Mathematical Objects
  • Matrix Multiplication and other Operations
  • Systems of Linear Equations
  • Linear Least Squares
  • Singular Value Decomposition

Taught by

Daniel Boley


3.0 rating, based on 1 Class Central review

4.1 rating at Coursera based on 235 ratings

Start your review of Matrix Methods

  • The following course offers some really good reference materials which helps in studying the mathematical concepts.
    The delivery is a lot like in a classrom , but the reference materials are actually wonderful!
    The instructors delivery is indeed sometimes quite boring.

    However, the SVD section of the course offers a pretty intutive and mathematical explanation of SVD! You will learn how small changes to the SVD matrix would effect the entire outcome ! The Least Squares Regression is also quite interesting.

    I would suggest Gilbert Strange Lin Alg lecs as a ref .

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