In a world increasingly dependent on technology, the ability to think deeply about the quantitative patterns around us is essential to understanding the implications of that technology. This course is designed to give you a framework for better understanding the three important classes of patterns we commonly encounter in this technologically oriented world: patterns in time, patterns in space, and patterns in data. The course develops a quantitative framework for understanding the nature of patterns, how they behave–and how they are created. We will draw on examples from across the disciplines, including population dynamics in biology, oscillations, vision and image processing, and pattern recognition in machine learning and perception.
While there is no formal prerequisite, students should be comfortable with senior high school mathematics. Consult the instructor if you are uncertain of your math background.
Note: This course is open to all Waterloo students, and meets one of the Math course requirements for BKI students.
most recent syllabus available from the Department of Knowledge Integration upon request