Our experience of the world is conveyed through our perception of patterns in our environment. The world around us changes over time, it changes as we move around in space, and we detect patterns of connection and relationships in more abstract data sets. While not all patterns are quantifiable, in order to understand those that are, we need a quantitative framework for the detection, description, analysis, and prediction of patterns in time, patterns in space, and patterns in data. This course introduces such a framework, touching on patterns of exponential growth and decay, oscillation, spatial spread and distribution, and classification and clustering of patterns represented by some number of quantitative features.
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.
most recent syllabus available from the Department of Knowledge Integration upon request