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University of Minnesota

Matrix Methods

University of Minnesota via Coursera

Overview

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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.

Syllabus

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

Taught by

Daniel Boley

Reviews

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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