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Linear Algebra for Machine Learning

Linear algebra provides a mathematical framework for organizing information and then using that information to solve problems, especially physics, math, engineering, or data analytics problems. Linear algebra is essential for understanding and creating machine learning algorithms, especially neural network and deep learning models.

In this course, you will learn the linear algebra skills necessary for machine learning and neural network modelling. The course starts off with a review of basic matrices and vector algebra as applied to linear systems. Then you will learn advanced skills for finding the highest and lowest points of systems, quantifying the degree of learning, and optimizing the speed of learning in vector spaces and linear transformations. The hands-on lessons and assignments will equip you with the mathematical background required to build and train simple neural networks.

Key topics:

  • A review of matrix algebra fundamentals: vectors, matrices, linear systems
  • Matrix Operations
  • Linear System, Solution Sets
  • Vector Spaces
  • Eigenvalues, Eigenvectors
  • Matrix inversion of non-square Matrices
  • Quadratic forms, gradient descent
  • Principal Component Analysis
  • Basics of TensorFlow

Practical experience:

  • Hands-on lab assignments and projects using various open-source software programs

Course typically offered: Quarterly, online.

Software: Students will use Octave, Caffe, and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.

Prerequisites: Introductory linear algebra. More specifically, knowledge and understanding of vectors, matrices, and three-dimensional coordinate systems.

Next steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.

Contact: For more information about this course, please contact

Course Number: CSE-41287
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods

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