Linear Algebra for Computing

Course Description

Linear Algebra for Computing introduces vectors, matrices, and transformations with a consistent eye toward their later use in graphics, machine learning, and data analysis. Students leave the course able to read and reason about the linear-algebra notation used throughout the 300-level AI and data courses.

Learning Outcomes

Perform and interpret core matrix and vector operations.
Solve systems of linear equations using standard methods.
Explain eigenvalues and eigenvectors and identify simple applications.
Connect linear algebra concepts to later coursework in machine learning and graphics.

Weekly Topics

Examines vectors & vector spaces from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend matrix operations, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on systems of linear equations with an in-class exercise students carry into the week's assignment.

Focuses on common mistakes and misconceptions around matrix inverses, using student work from the previous assignment as material.

Introduces determinants and immediately puts it to use in a small design or implementation task.

Situates linear transformations within the broader arc of the course, showing how it connects to what comes next.

Uses a guest dataset or scenario to explore mid-semester review in a setting closer to professional practice.

Introduces eigenvalues & eigenvectors through short lectures and worked examples, building the vocabulary the rest of the course relies on.

Works through diagonalization in a lab-driven session, with guided exercises students complete and discuss in small groups.

Covers applications in graphics in depth, connecting the underlying theory to a concrete example the class builds together.

Examines applications in data analysis from a practical angle, using a case drawn from real systems to motivate the concepts.

Builds directly on the previous week to extend applied problem set, with an emphasis on where the earlier techniques stop working.

Combines a short lecture on revision with an in-class exercise students carry into the week's assignment.

Assessment

Assignments — 15%

Practical Work — 5%

Mid-Semester — 30%

Final Examination — 50%

Prerequisite Map

Related Courses

Core200 Level

STA201

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Probability & Statistics for Computing gives students the statistical toolkit that later machine learning, data systems, and empirical research coursework assumes: probability distributions, estimation, hypothesis testing, and enough statistical skepticism to question a dataset before trusting it.

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Sofia Reyes 4 cr