Linear Algebra for Machine Learning

Master the Math Powering Machine Learning and Neural Networks

Linear algebra is the foundation of machine learning and artificial intelligence. It provides the mathematical tools used to represent data, build models, and understand how neural networks and deep learning algorithms work.

In this course, you will learn essential linear algebra concepts for machine learning and neural network modeling, starting with vectors and matrices and progressing to advanced topics such as dimensionality reduction and optimization techniques used in Large Language Models like GPT. Through hands-on lessons and practical assignments, you will gain the skills needed to build and train simple neural networks and apply linear algebra to real-world AI and data science problems.

Course Highlights

• Tensor operations for data representation and neural networks (MLP)
• Matrix representation and solutions of linear systems for model weights
• Vector spaces, basis, and linear transformations for text and image data
• Linear regression using matrix inversion
• Gradient descent for neural network optimization
• Singular Value Decomposition (SVD) for image compression and reconstruction
• Dimensionality reduction and Principal Component Analysis (PCA)
• TensorFlow regression models
• Hands-on lab assignments and projects using various open-source software programs (Octave/Python)

Course Benefits

By the end of this course, you will be able to:

• Build a strong mathematical foundation for machine learning and AI
• Understand how neural networks work instead of treating them as black boxes
• Apply linear algebra concepts to real-world data science problems
• Prepare for advanced topics in deep learning and Large Language Models
• Strengthen problem-solving and analytical skills for technical careers
• Credit earned may be applied toward an academic degree or professional credential, subject to the approval of the receiving institution(s).

Course Details and Next Steps

• Course Typically Offered: Online, every quarter
• Software: Students will use Octave/Python and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.
• Prerequisites: Understanding of college-level algebra and calculus
• Next steps:  Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.
• More information: For more information about this course, please contact unex-techdata@ucsd.edu