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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. 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.

Key topics:

  • Fundamentals of matrix algebra: vectors, matrices, tensors
  • Multiplying matrices and vectors
  • Identity and inverse matrices
  • Linear dependence and span
  • Norms
  • Dimensions and hyperplanes
  • Conjugate gradients
  • Eigenvalues, Eigenvectors, Eigendecomposition
  • Principal Component Analysis
  • Basics of TensorFlow

Practical experience:

  • 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.

Prerequisites: Strong understanding of college-level algebra and calculus required.

Next steps: Upon completion, consider taking courses in our Data Mining for Advanced Analytics program to continue learning.    

Contact: For more information about this course, please contact unex-techdata@ucsd.edu.

Course Number: CSE-41287
Credit: 3.00 unit(s)

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