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AI for students lesson 23: Design a tiny AI project · Set 569 · 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.❌ Tara’s fruit sorter was tested on 48 new fruit photos and got 44 right. How many did it get wrong?
  2. 2.❌ Ishaan’s handwriting reader was tested on 15 new handwritten letters and got 13 right. How many did it get wrong?
  3. 3.❌ Kabir’s fruit sorter was tested on 35 new fruit photos and got 28 right. How many did it get wrong?
  4. 4.📋 Planning an AI project: which comes first — let it learn patterns or fix it and test again? (a) let it learn patterns (b) fix it and test again
  5. 5.📋 Planning an AI project: which comes first — improve data, retest or work out the accuracy? (a) improve data, retest (b) work out the accuracy
  6. 6.🧪 Why must you test a model on examples it has NOT seen? (a) to make it faster (b) to save paper (c) to make it bigger (d) to see if it learned
  7. 7.❌ Vihaan’s recycling sorter was tested on 32 new waste photos and got 24 right. How many did it get wrong?
  8. 8.📋 Planning an AI project: which comes first — let it learn patterns or test on new photos? (a) let it learn patterns (b) test on new photos
  9. 9.⚖️ Two handwriting reader models were tested on the same 25 handwritten letters. Model A got 22 right and Model B got 14 right. How many percentage points more accurate is Model A?
  10. 10.🎯 Rohan’s handwriting reader is 60% accurate. If it is tested on 10 new handwritten letters, how many should it get right?
  11. 11.❌ Vihaan’s spam spotter was tested on 35 new messages and got 26 right. How many did it get wrong?
  12. 12.🧪 What is accuracy? (a) correct ÷ total × 100 (b) total × 2 (c) total − correct (d) correct + total
  13. 13.🎯 Vihaan’s bird-song app is 50% accurate. If it is tested on 90 new bird songs, how many should it get right?
  14. 14.🎯 Ananya’s leaf identifier was tested on 4 new leaf photos and got 3 right. What is its accuracy in percent?
  15. 15.📋 Planning an AI project: which comes first — pick a question to solve or gather many examples? (a) pick a question to solve (b) gather many examples
  16. 16.❌ Ananya’s fruit sorter was tested on 19 new fruit photos and got 10 right. How many did it get wrong?
  17. 17.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a new colour screen (b) more labelled examples (c) a louder speaker (d) fewer examples
  18. 18.📋 Planning an AI project: which comes first — try unseen examples or split train and test? (a) try unseen examples (b) split train and test
  19. 19.📋 Planning an AI project: which comes first — train the model or set aside test photos? (a) train the model (b) set aside test photos
  20. 20.📋 Planning an AI project: which comes first — tag each photo by type or improve data, retest? (a) tag each photo by type (b) improve data, retest
  21. 21.📋 Planning an AI project: which comes first — calculate percent right or pick a question to solve? (a) calculate percent right (b) pick a question to solve
  22. 22.❌ Diya’s fruit sorter was tested on 47 new fruit photos and got 45 right. How many did it get wrong?
  23. 23.🎯 Kabir’s bird-song app was tested on 4 new bird songs and got 3 right. What is its accuracy in percent?
  24. 24.🎯 Diya’s spam spotter is 50% accurate. If it is tested on 70 new messages, how many should it get right?
  25. 25.🎯 Rohan’s fruit sorter is 60% accurate. If it is tested on 10 new fruit photos, how many should it get right?
  26. 26.📋 Planning an AI project: which comes first — collect fruit photos or label each example? (a) collect fruit photos (b) label each example
  27. 27.❌ Ananya’s handwriting reader was tested on 25 new handwritten letters and got 21 right. How many did it get wrong?
  28. 28.📋 Planning an AI project: which comes first — train the model or recheck every label? (a) train the model (b) recheck every label
  29. 29.🎯 Ananya’s fruit sorter is 70% accurate. If it is tested on 80 new fruit photos, how many should it get right?
  30. 30.🎯 Nisha’s leaf identifier is 80% accurate. If it is tested on 30 new leaf photos, how many should it get right?
  31. 31.📋 Planning an AI project: which comes first — train the model or fix it and test again? (a) train the model (b) fix it and test again
  32. 32.🎯 Aarav’s leaf identifier was tested on 5 new leaf photos and got 3 right. What is its accuracy in percent?
  33. 33.📋 Planning an AI project: which comes first — split train and test or tag each photo by type? (a) split train and test (b) tag each photo by type
  34. 34.🧪 You can plan a simple AI project with… (a) only a big robot (b) paper and examples (c) nothing at all (d) a secret password
  35. 35.⚖️ Two leaf identifier models were tested on the same 20 leaf photos. Model A got 17 right and Model B got 13 right. How many percentage points more accurate is Model A?
  36. 36.❌ Meera’s spam spotter was tested on 17 new messages and got 15 right. How many did it get wrong?
  37. 37.🎯 Nisha’s bird-song app is 70% accurate. If it is tested on 10 new bird songs, how many should it get right?
  38. 38.❌ Vihaan’s spam spotter was tested on 44 new messages and got 40 right. How many did it get wrong?
  39. 39.🎯 Saanvi’s fruit sorter is 80% accurate. If it is tested on 100 new fruit photos, how many should it get right?
  40. 40.⚖️ Two handwriting reader models were tested on the same 50 handwritten letters. Model A got 45 right and Model B got 40 right. How many percentage points more accurate is Model A?

Answer key

  1. 4
  2. 2
  3. 7
  4. let it learn patterns
  5. work out the accuracy
  6. to see if it learned
  7. 8
  8. let it learn patterns
  9. 32
  10. 6
  11. 9
  12. correct ÷ total × 100
  13. 45
  14. 75
  15. pick a question to solve
  16. 9
  17. more labelled examples
  18. split train and test
  19. set aside test photos
  20. tag each photo by type
  21. pick a question to solve
  22. 2
  23. 75
  24. 35
  25. 6
  26. collect fruit photos
  27. 4
  28. recheck every label
  29. 56
  30. 24
  31. train the model
  32. 60
  33. tag each photo by type
  34. paper and examples
  35. 20
  36. 2
  37. 7
  38. 4
  39. 80
  40. 10

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