This November workshop spotlights classification, how computers sort information into groups so they can make sense of the world. We’ll start from what learners already do naturally: noticing patterns and naming things. Beginners handle tangible sorting challenges (fruits, animals, shapes) and convert their human logic into code using clear features (colour, size, edges), explicit rules (if/then), and simple labels (“mammal,” “triangle,” “spam”). Using a repeatable cycle, See → Name → Rule → Test → Improve—they’ll test their rules on examples, measure what works, and refine until their mini-classifier gives reliable results, building confidence and clear reasoning along the way.
Advanced learners push further with decision trees and probability-style choices, comparing different splits, setting thresholds, and weighing trade-offs (catch more spam vs. avoid false alarms). Short, hands-on labs connect classroom logic to real systems like spam filters, recommendation engines, and safety checks, while quick ethics prompts introduce bias, fairness, and why humans stay in the loop. By the end, beginners ship a small program or flowchart that turns inputs into labels with an explanation; advanced learners deliver a mini decision-tree classifier plus a brief results note on accuracy and improvements.
