Weighted Models: Learned Parameters and Written Code · seed 1 · A4, ink-friendly. The answer key prints on its own page for grown-ups.
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How learned weights work with code

Computing · Artificial Intelligence · ages 14-15
Name ______________________   Date ____________

  1. What does a weight do in the toy spam model?

    • Stores a complete written explanation for every email
    • Multiplies an input before the total is added
    • Deletes the threshold comparison from the calculation

  2. What distinguishes fitted weights from a hand-written spam rule?

    • Weights require no code to calculate a prediction
    • Weights are learned numerical parameters
    • Weights are complete rules typed separately for each email

  3. Which part stays fixed while this toy model's weights and bias are fitted?

    • The current value of every fitted weight
    • The current numerical value of the bias
    • The multiply-and-add calculation

  4. How could you investigate why the small toy model marked a message as spam?

    • Assume fitted numbers make every calculation impossible to inspect
    • Trace the inputs, weights and calculation
    • Read a separate hand-written rule stored for that exact message

  5. During training, the toy model's prediction differs from a correct example label. Which action fits its learning process?

    • Remove every example whose prediction differs from its label
    • Replace the whole calculation with a new one after each error
    • Adjust weights and bias using the error

  6. Training has stopped. The model now only predicts, with no parameter-update process running. What happens to its weights?

    • They keep changing automatically with each new prediction
    • They reset to zero whenever a new message arrives
    • They stay fixed during prediction

  7. Retraining the toy model on more examples can change its predictions without changing its fixed calculation.

    Circle one:   True   False

  8. A vendor says its model has learned weights, so there is no code or calculation to inspect. Which response is best supported?

    • Ask how learned weights and code produce predictions
    • Assume every prediction has a separate rule typed by a person
    • Assume fitted weights mean there is no software to examine
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Answer key

For grown-ups. Fold this page away before handing over the rest.

How learned weights work with code W1-mt_3_1toZUxak-s1

  1. Multiplies an input before the total is added · Each input is multiplied by its weight. Those products are then added with the bias.
  2. Weights are learned numerical parameters · Training fits numerical weights from examples. Software still defines how those weights produce a prediction.
  3. The multiply-and-add calculation · This toy model keeps its calculation fixed while training adjusts parameter values.
  4. Trace the inputs, weights and calculation · The toy model's inputs and parameters can be followed through its calculation to its prediction.
  5. Adjust weights and bias using the error · Training uses the prediction error to adjust parameters. The adjustment aims to reduce errors; it is not a promise that every prediction improves.
  6. They stay fixed during prediction · Prediction alone uses the fitted weights. The question rules out training or other parameter updates.
  7. True · Retraining can fit different parameter values. The same calculation can then produce different predictions.
  8. Ask how learned weights and code produce predictions · Learned weights are used by a defined calculation. Fitted parameters do not remove code that can be examined.
Worksheet · LightMySky