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Computers & Digital Literacy · Grade 9

Data, Labels and Problem Framing

Learners sort machine-learning requests into classification, regression and clustering, read a dataset as examples, features and a label, and learn to ask what a label really measures. They then frame projects before any model exists: the decision, the people affected, the cost of each error, a success criterion and a baseline. The course ends by deciding when a simple rule, more data or human judgment is the better choice.

  • 4 lessons
  • About 100 minutes
  • Framing check: 9 questions
  • Ways to answer: fill in the blanks, choose an answer, highlight a text, match pairs, type a number and put in order
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What learners will discover

  • Distinguish classification, regression and clustering tasks by asking whether examples have a label and whether that label is a category or a quantity.
  • Identify examples, features and the label in a dataset, exclude identifiers and information not known at prediction time, and recognize when a label is a proxy.
  • Frame a machine-learning problem by stating the decision, the people affected, the cost of each error, a success criterion and a baseline before modeling.
  • Decide when not to use machine learning (a clear rule exists, the data cannot support learning, or high stakes lack oversight) and justify the choice.

Lessons

  1. Three kinds of learning tasks 7 steps4 practiceabout 24 min
  2. Features, labels and proxies 7 steps4 practiceabout 26 min
  3. Frame the problem and define success first 6 steps4 practiceabout 26 min
  4. When not to use machine learning 5 steps2 practiceabout 24 min
  5. Framing check 9 questions at the end

Keep exploring

More 9th grade computer skills

Guide for grown-ups