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AI for students lesson 23: Design a tiny AI project · Set 911 · 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.❌ Meera’s fruit sorter was tested on 36 new fruit photos and got 27 right. How many did it get wrong?
  2. 2.🧪 Why must you test a model on examples it has NOT seen? (a) to make it bigger (b) to make it faster (c) to see if it learned (d) to save paper
  3. 3.🎯 Tara’s fruit sorter was tested on 5 new fruit photos and got 3 right. What is its accuracy in percent?
  4. 4.📋 Planning an AI project: which comes first — let it learn patterns or work out the accuracy? (a) let it learn patterns (b) work out the accuracy
  5. 5.🧪 You can plan a simple AI project with… (a) a secret password (b) nothing at all (c) paper and examples (d) only a big robot
  6. 6.📋 Planning an AI project: which comes first — improve data, retest or recheck every label? (a) improve data, retest (b) recheck every label
  7. 7.❌ Vihaan’s handwriting reader was tested on 16 new handwritten letters and got 13 right. How many did it get wrong?
  8. 8.🎯 Kabir’s bird-song app was tested on 20 new bird songs and got 13 right. What is its accuracy in percent?
  9. 9.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) fewer examples (b) a new colour screen (c) a louder speaker (d) more labelled examples
  10. 10.📋 Planning an AI project: which comes first — fix it and test again or work out the accuracy? (a) fix it and test again (b) work out the accuracy
  11. 11.❌ Kabir’s handwriting reader was tested on 45 new handwritten letters and got 44 right. How many did it get wrong?
  12. 12.⚖️ Two spam spotter models were tested on the same 10 messages. Model A got 8 right and Model B got 7 right. How many percentage points more accurate is Model A?
  13. 13.📋 Planning an AI project: which comes first — improve data, retest or correct bad labels? (a) improve data, retest (b) correct bad labels
  14. 14.📋 Planning an AI project: which comes first — correct bad labels or set aside test photos? (a) correct bad labels (b) set aside test photos
  15. 15.🎯 Saanvi’s spam spotter was tested on 5 new messages and got 3 right. What is its accuracy in percent?
  16. 16.📋 Planning an AI project: which comes first — pick a question to solve or set aside test photos? (a) pick a question to solve (b) set aside test photos
  17. 17.🎯 Arjun’s recycling sorter was tested on 4 new waste photos and got 4 right. What is its accuracy in percent?
  18. 18.🎯 Arjun’s handwriting reader was tested on 25 new handwritten letters and got 21 right. What is its accuracy in percent?
  19. 19.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 47 right and Model B got 42 right. How many percentage points more accurate is Model A?
  20. 20.📋 Planning an AI project: which comes first — label each example or recheck every label? (a) label each example (b) recheck every label
  21. 21.📦 Saanvi collected 40 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
  22. 22.📦 Rohan collected 90 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
  23. 23.📋 Planning an AI project: which comes first — choose what to sort or let it learn patterns? (a) choose what to sort (b) let it learn patterns
  24. 24.🎯 Ananya’s handwriting reader is 90% accurate. If it is tested on 30 new handwritten letters, how many should it get right?
  25. 25.🧪 What is accuracy? (a) total × 2 (b) correct + total (c) correct ÷ total × 100 (d) total − correct
  26. 26.🎯 Vihaan’s handwriting reader is 90% accurate. If it is tested on 30 new handwritten letters, how many should it get right?
  27. 27.❌ Kabir’s handwriting reader was tested on 42 new handwritten letters and got 35 right. How many did it get wrong?
  28. 28.📦 Saanvi collected 50 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
  29. 29.📋 Planning an AI project: which comes first — set aside test photos or try unseen examples? (a) set aside test photos (b) try unseen examples
  30. 30.📋 Planning an AI project: which comes first — try unseen examples or work out the accuracy? (a) try unseen examples (b) work out the accuracy
  31. 31.📋 Planning an AI project: which comes first — work out the accuracy or try unseen examples? (a) work out the accuracy (b) try unseen examples
  32. 32.⚖️ Two bird-song app models were tested on the same 20 bird songs. Model A got 17 right and Model B got 15 right. How many percentage points more accurate is Model A?
  33. 33.🎯 Tara’s bird-song app was tested on 4 new bird songs and got 2 right. What is its accuracy in percent?
  34. 34.🎯 Meera’s fruit sorter was tested on 20 new fruit photos and got 10 right. What is its accuracy in percent?
  35. 35.📋 Planning an AI project: which comes first — train the model or try unseen examples? (a) train the model (b) try unseen examples
  36. 36.🎯 Rohan’s fruit sorter was tested on 10 new fruit photos and got 8 right. What is its accuracy in percent?
  37. 37.📦 Meera collected 120 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
  38. 38.📦 Diya collected 50 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
  39. 39.📋 Planning an AI project: which comes first — test on new photos or choose what to sort? (a) test on new photos (b) choose what to sort
  40. 40.🎯 Kabir’s leaf identifier was tested on 25 new leaf photos and got 13 right. What is its accuracy in percent?

Answer key

  1. 9
  2. to see if it learned
  3. 60
  4. let it learn patterns
  5. paper and examples
  6. recheck every label
  7. 3
  8. 65
  9. more labelled examples
  10. work out the accuracy
  11. 1
  12. 10
  13. correct bad labels
  14. correct bad labels
  15. 60
  16. pick a question to solve
  17. 100
  18. 84
  19. 10
  20. label each example
  21. 32
  22. 72
  23. choose what to sort
  24. 27
  25. correct ÷ total × 100
  26. 27
  27. 7
  28. 40
  29. set aside test photos
  30. try unseen examples
  31. try unseen examples
  32. 10
  33. 50
  34. 50
  35. train the model
  36. 80
  37. 96
  38. 40
  39. choose what to sort
  40. 52

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