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AI for students lesson 23: Design a tiny AI project · Set 220 · 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.📋 Planning an AI project: which comes first — collect fruit photos or choose what to sort? (a) collect fruit photos (b) choose what to sort
  2. 2.📋 Planning an AI project: which comes first — improve data, retest or set aside test photos? (a) improve data, retest (b) set aside test photos
  3. 3.❌ Aarav’s bird-song app was tested on 26 new bird songs and got 25 right. How many did it get wrong?
  4. 4.🎯 Ishaan’s fruit sorter was tested on 50 new fruit photos and got 40 right. What is its accuracy in percent?
  5. 5.🧪 You can plan a simple AI project with… (a) nothing at all (b) paper and examples (c) a secret password (d) only a big robot
  6. 6.❌ Aarav’s bird-song app was tested on 37 new bird songs and got 29 right. How many did it get wrong?
  7. 7.📋 Planning an AI project: which comes first — label each example or split train and test? (a) label each example (b) split train and test
  8. 8.📋 Planning an AI project: which comes first — pick a question to solve or test on new photos? (a) pick a question to solve (b) test on new photos
  9. 9.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) more labelled examples (b) a louder speaker (c) fewer examples (d) a new colour screen
  10. 10.🧪 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
  11. 11.❌ Meera’s spam spotter was tested on 22 new messages and got 15 right. How many did it get wrong?
  12. 12.🎯 Nisha’s spam spotter is 60% accurate. If it is tested on 50 new messages, how many should it get right?
  13. 13.🎯 Saanvi’s leaf identifier was tested on 25 new leaf photos and got 23 right. What is its accuracy in percent?
  14. 14.🎯 Tara’s fruit sorter was tested on 25 new fruit photos and got 14 right. What is its accuracy in percent?
  15. 15.🎯 Meera’s spam spotter is 90% accurate. If it is tested on 30 new messages, how many should it get right?
  16. 16.📋 Planning an AI project: which comes first — improve data, retest or split train and test? (a) improve data, retest (b) split train and test
  17. 17.❌ Aarav’s leaf identifier was tested on 49 new leaf photos and got 47 right. How many did it get wrong?
  18. 18.🎯 Aarav’s fruit sorter is 50% accurate. If it is tested on 10 new fruit photos, how many should it get right?
  19. 19.🎯 Nisha’s spam spotter is 80% accurate. If it is tested on 50 new messages, how many should it get right?
  20. 20.🎯 Saanvi’s recycling sorter was tested on 50 new waste photos and got 29 right. What is its accuracy in percent?
  21. 21.📋 Planning an AI project: which comes first — calculate percent right or set aside test photos? (a) calculate percent right (b) set aside test photos
  22. 22.📋 Planning an AI project: which comes first — set aside test photos or correct bad labels? (a) set aside test photos (b) correct bad labels
  23. 23.📋 Planning an AI project: which comes first — train the model or tag each photo by type? (a) train the model (b) tag each photo by type
  24. 24.🎯 Meera’s recycling sorter was tested on 4 new waste photos and got 3 right. What is its accuracy in percent?
  25. 25.❌ Diya’s leaf identifier was tested on 41 new leaf photos and got 37 right. How many did it get wrong?
  26. 26.🎯 Ananya’s bird-song app is 60% accurate. If it is tested on 100 new bird songs, how many should it get right?
  27. 27.📋 Planning an AI project: which comes first — fix it and test again or collect fruit photos? (a) fix it and test again (b) collect fruit photos
  28. 28.🎯 Saanvi’s handwriting reader was tested on 4 new handwritten letters and got 2 right. What is its accuracy in percent?
  29. 29.🧪 What is accuracy? (a) total − correct (b) correct ÷ total × 100 (c) correct + total (d) total × 2
  30. 30.📦 Meera collected 50 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
  31. 31.📦 Aarav collected 200 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
  32. 32.🎯 Diya’s bird-song app is 60% accurate. If it is tested on 90 new bird songs, how many should it get right?
  33. 33.🎯 Ishaan’s fruit sorter was tested on 5 new fruit photos and got 5 right. What is its accuracy in percent?
  34. 34.📋 Planning an AI project: which comes first — split train and test or recheck every label? (a) split train and test (b) recheck every label
  35. 35.📦 Rohan collected 60 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
  36. 36.📦 Ananya collected 160 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
  37. 37.📋 Planning an AI project: which comes first — work out the accuracy or pick a question to solve? (a) work out the accuracy (b) pick a question to solve
  38. 38.📋 Planning an AI project: which comes first — gather many examples or test on new photos? (a) gather many examples (b) test on new photos
  39. 39.🎯 Tara’s recycling sorter was tested on 10 new waste photos and got 9 right. What is its accuracy in percent?
  40. 40.📦 Ishaan collected 60 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?

Answer key

  1. choose what to sort
  2. set aside test photos
  3. 1
  4. 80
  5. paper and examples
  6. 8
  7. label each example
  8. pick a question to solve
  9. more labelled examples
  10. to see if it learned
  11. 7
  12. 30
  13. 92
  14. 56
  15. 27
  16. split train and test
  17. 2
  18. 5
  19. 40
  20. 58
  21. set aside test photos
  22. correct bad labels
  23. tag each photo by type
  24. 75
  25. 4
  26. 60
  27. collect fruit photos
  28. 50
  29. correct ÷ total × 100
  30. 40
  31. 160
  32. 54
  33. 100
  34. recheck every label
  35. 48
  36. 128
  37. pick a question to solve
  38. gather many examples
  39. 90
  40. 48

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