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. You will begin by learning overview 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.
- Fundamentals of matrix algebra: 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 Tensor Flow
- Hands-on lab assignments and projects using various open-source software programs
Course typically offered: Online in Fall, Winter, Spring, and Summer
Software: Students will use Octave, Caffe, and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.
Optional reading: A college-level Linear Algebra book
Prerequisites: Students should already be comfortable with linear algebra as this course will only provide a brief review of the subject.
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 firstname.lastname@example.org.
Course Number: CSE-41287
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods
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4/9/2019 - 6/8/2019
6/24/2019 - 8/23/2019