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Machine Learning Foundations / Module 1

Module 1 check

Module 1 Assessment: Problem Framing Check

Assessment ID: ML-M01-QA01 Estimated active time: 20-30 minutes Status: Draft

Part A: Short checks

Answer in one or two sentences.

  1. Why should an ML project not start by choosing an algorithm?
  2. What is the difference between a target and a practical goal?
  3. What is the unit of analysis?
  4. Why does prediction time matter?
  5. Give one example of a no-model option.
  6. What is one way a model can harm an affected person even if it helps the direct user?

Part B: Classify the task

Classify each request as prediction, description, automation, decision support, or a combination.

RequestClassificationReason
Show which module has the highest drop-off.
Predict which learner-module attempts may need mentor support.
Send a reminder after seven inactive days.
Show mentors a review list with risk notes and limits.

Part C: Repair the problem frame

Weak frame:

Use ML to improve course completion.

Rewrite it using this structure:

At [prediction time], predict whether [unit] will [target] by [future time], so [user] can [action].

Then identify:

FieldAnswer
Target
Unit of analysis
Prediction time
Outcome window
User
Action

Part D: Baseline and risk

For your repaired frame:

  1. Name one simple rule baseline.
  2. Name one descriptive-analysis alternative.
  3. Name one unacceptable failure.
  4. Decide whether the project should continue, be revised, or stop for now.

Rubric

LevelEvidence
PassThe answer defines target, unit, timing, action, user, affected people, baseline, and one serious failure mode. It avoids causal overclaiming.
ReviseThe answer has a useful goal but one or more core fields are vague, late data is used, or the action is unclear.
Not yetThe answer mainly says to build a model, choose an algorithm, or improve a broad outcome without defining the prediction and use context.

Expected strong answer qualities

  • Uses simple, concrete language.
  • Names one prediction per row or case.
  • Keeps prediction before action.
  • Compares ML with a simpler option.
  • Includes a reason not to build or not to use the model.
  • Does not use real personal or sensitive data.