CCOG for MUC 252 Winter 2025
- Course Number:
- MUC 252
- Course Title:
- Computer Vision
- Credit Hours:
- 4
- Lecture Hours:
- 40
- Lecture/Lab Hours:
- 0
- Lab Hours:
- 0
Course Description
Addendum to Course Description
Computer Vision is commonly associated with current approaches to AI and Machine Learning, but is also a field of research with decades of history. This course intends to leverage the history of Computer Vision as a narrative through which to explore beginner and intermediate coding topics, and explore general mathematics concepts with an interactive, code-forward approach. This course will build confidence through exercises in using code to solve problems, while invoking current themes in AI within a Humanities context.
Intended Outcomes for the course
Upon completion of the course students should be able to:
- Recount history of computer vision in context of power relations, connecting computational methods, their proliferation and cultural impact.
- Identify use cases, potential ethical or social concerns, and common methods of deriving information from digital images.
- Implement algorithms that transform and filter digital images to produce data.
- Assess emergent outcomes of different ways of solving problems within large systems.
Course Activities and Design
- Code Notebooks
- Group/Pair Programming
- Readings and Case Studies
- Interactive Software
- Process Visualizations
Outcome Assessment Strategies
- Code Review
- Class Participation
- Assignments
- Projects
Course Content (Themes, Concepts, Issues and Skills)
History of using technology to interpret images
Structure of digital representations of images
Convolution
Matrix operations
Images as digital signals
Optical Character Recognition
Eigen Faces
Probability
Statistics