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Matrix Methods

Offered By: University of Minnesota via Coursera

Tags

Linear Algebra Courses Python Courses Dimensionality Reduction Courses Noise Reduction Courses Principal Component Analysis Courses Singular Value Decomposition Courses Orthogonality Courses

Course Description

Overview

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

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