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AI for students lesson 23: Design a tiny AI project · Set 948 · 40 questions
TalentJR

AI tip (Accuracy = correct ÷ total × 100): Steps: 1 choose a problem → 2 collect examples → 3 label them → 4 check and correct bad labels (clean the data) → 5 split into training and test sets → 6 train → 7 test with new examples → 8 measure accuracy → 9 improve the data and retest. Accuracy = correct ÷ total × 100. Wrong = total − correct. Expected right = accuracy ÷ 100 × total. Keep some examples apart for testing (for example one fifth); the rest are for training.

NameDateTime takenScore ___ / 40
  1. 1.🧪 You can plan a simple AI project with… (a) paper and examples (b) nothing at all (c) a secret password (d) only a big robot
  2. 2.❌ Arjun’s spam spotter was tested on 13 new messages and got 11 right. How many did it get wrong?
  3. 3.🧪 Why must you test a model on examples it has NOT seen? (a) to see if it learned (b) to make it faster (c) to save paper (d) to make it bigger
  4. 4.📋 Planning an AI project: which comes first — train the model or split train and test? (a) train the model (b) split train and test
  5. 5.🎯 Meera’s leaf identifier was tested on 25 new leaf photos and got 25 right. What is its accuracy in percent?
  6. 6.⚖️ Two handwriting reader models were tested on the same 20 handwritten letters. Model A got 17 right and Model B got 15 right. How many percentage points more accurate is Model A?
  7. 7.📋 Planning an AI project: which comes first — try unseen examples or collect fruit photos? (a) try unseen examples (b) collect fruit photos
  8. 8.❌ Tara’s fruit sorter was tested on 39 new fruit photos and got 33 right. How many did it get wrong?
  9. 9.🎯 Rohan’s recycling sorter is 50% accurate. If it is tested on 80 new waste photos, how many should it get right?
  10. 10.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) fewer examples (b) more labelled examples (c) a new colour screen (d) a louder speaker
  11. 11.📋 Planning an AI project: which comes first — choose what to sort or collect fruit photos? (a) choose what to sort (b) collect fruit photos
  12. 12.📋 Planning an AI project: which comes first — correct bad labels or try unseen examples? (a) correct bad labels (b) try unseen examples
  13. 13.🎯 Kabir’s bird-song app was tested on 5 new bird songs and got 5 right. What is its accuracy in percent?
  14. 14.🧪 What is accuracy? (a) total − correct (b) correct ÷ total × 100 (c) correct + total (d) total × 2
  15. 15.📋 Planning an AI project: which comes first — tag each photo by type or collect fruit photos? (a) tag each photo by type (b) collect fruit photos
  16. 16.📦 Meera collected 50 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
  17. 17.❌ Ishaan’s handwriting reader was tested on 47 new handwritten letters and got 40 right. How many did it get wrong?
  18. 18.📋 Planning an AI project: which comes first — gather many examples or test on new photos? (a) gather many examples (b) test on new photos
  19. 19.📋 Planning an AI project: which comes first — recheck every label or train the model? (a) recheck every label (b) train the model
  20. 20.❌ Vihaan’s handwriting reader was tested on 49 new handwritten letters and got 44 right. How many did it get wrong?
  21. 21.📋 Planning an AI project: which comes first — test on new photos or train the model? (a) test on new photos (b) train the model
  22. 22.🎯 Meera’s recycling sorter is 70% accurate. If it is tested on 10 new waste photos, how many should it get right?
  23. 23.🎯 Kabir’s leaf identifier was tested on 4 new leaf photos and got 2 right. What is its accuracy in percent?
  24. 24.📋 Planning an AI project: which comes first — let it learn patterns or correct bad labels? (a) let it learn patterns (b) correct bad labels
  25. 25.❌ Ishaan’s spam spotter was tested on 14 new messages and got 13 right. How many did it get wrong?
  26. 26.📋 Planning an AI project: which comes first — let it learn patterns or split train and test? (a) let it learn patterns (b) split train and test
  27. 27.🎯 Tara’s bird-song app is 70% accurate. If it is tested on 50 new bird songs, how many should it get right?
  28. 28.❌ Ishaan’s recycling sorter was tested on 20 new waste photos and got 19 right. How many did it get wrong?
  29. 29.🎯 Saanvi’s bird-song app was tested on 10 new bird songs and got 9 right. What is its accuracy in percent?
  30. 30.📦 Kabir collected 60 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
  31. 31.📋 Planning an AI project: which comes first — gather many examples or split train and test? (a) gather many examples (b) split train and test
  32. 32.📋 Planning an AI project: which comes first — try unseen examples or calculate percent right? (a) try unseen examples (b) calculate percent right
  33. 33.📋 Planning an AI project: which comes first — calculate percent right or fix it and test again? (a) calculate percent right (b) fix it and test again
  34. 34.🎯 Meera’s fruit sorter was tested on 25 new fruit photos and got 21 right. What is its accuracy in percent?
  35. 35.📋 Planning an AI project: which comes first — train the model or test on new photos? (a) train the model (b) test on new photos
  36. 36.🎯 Diya’s fruit sorter was tested on 25 new fruit photos and got 25 right. What is its accuracy in percent?
  37. 37.🎯 Meera’s fruit sorter was tested on 10 new fruit photos and got 9 right. What is its accuracy in percent?
  38. 38.🎯 Meera’s recycling sorter is 50% accurate. If it is tested on 60 new waste photos, how many should it get right?
  39. 39.📋 Planning an AI project: which comes first — fix it and test again or calculate percent right? (a) fix it and test again (b) calculate percent right
  40. 40.📋 Planning an AI project: which comes first — calculate percent right or improve data, retest? (a) calculate percent right (b) improve data, retest

Answer key

  1. paper and examples
  2. 2
  3. to see if it learned
  4. split train and test
  5. 100
  6. 10
  7. collect fruit photos
  8. 6
  9. 40
  10. more labelled examples
  11. choose what to sort
  12. correct bad labels
  13. 100
  14. correct ÷ total × 100
  15. collect fruit photos
  16. 40
  17. 7
  18. gather many examples
  19. recheck every label
  20. 5
  21. train the model
  22. 7
  23. 50
  24. correct bad labels
  25. 1
  26. split train and test
  27. 35
  28. 1
  29. 90
  30. 48
  31. gather many examples
  32. try unseen examples
  33. calculate percent right
  34. 84
  35. train the model
  36. 100
  37. 90
  38. 30
  39. calculate percent right
  40. calculate percent right

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