Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 751 · 40 questions

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.

1. 1.🎯 Aarav’s fruit sorter was tested on 25 new fruit photos and got 22 right. What is its accuracy in percent?
2. 2.🧪 What is accuracy? (a) total − correct (b) correct + total (c) total × 2 (d) correct ÷ total × 100
3. 3.🧪 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
4. 4.📋 Planning an AI project: which comes first — test on new photos or work out the accuracy? (a) test on new photos (b) work out the accuracy
5. 5.📦 Rohan collected 80 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
6. 6.🎯 Kabir’s fruit sorter was tested on 4 new fruit photos and got 2 right. What is its accuracy in percent?
7. 7.🧪 Why must you test a model on examples it has NOT seen? (a) to make it faster (b) to see if it learned (c) to make it bigger (d) to save paper
8. 8.🎯 Saanvi’s handwriting reader was tested on 4 new handwritten letters and got 2 right. What is its accuracy in percent?
9. 9.📋 Planning an AI project: which comes first — let it learn patterns or try unseen examples? (a) let it learn patterns (b) try unseen examples
10. 10.📋 Planning an AI project: which comes first — improve data, retest or train the model? (a) improve data, retest (b) train the model
11. 11.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a louder speaker (b) more labelled examples (c) fewer examples (d) a new colour screen
12. 12.🎯 Rohan’s recycling sorter was tested on 5 new waste photos and got 3 right. What is its accuracy in percent?
13. 13.🎯 Saanvi’s handwriting reader was tested on 4 new handwritten letters and got 4 right. What is its accuracy in percent?
14. 14.📋 Planning an AI project: which comes first — label each example or choose what to sort? (a) label each example (b) choose what to sort
15. 15.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 40 right and Model B got 36 right. How many percentage points more accurate is Model A?
16. 16.📋 Planning an AI project: which comes first — label each example or correct bad labels? (a) label each example (b) correct bad labels
17. 17.🎯 Kabir’s handwriting reader is 90% accurate. If it is tested on 60 new handwritten letters, how many should it get right?
18. 18.📋 Planning an AI project: which comes first — collect fruit photos or pick a question to solve? (a) collect fruit photos (b) pick a question to solve
19. 19.📋 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
20. 20.⚖️ Two fruit sorter models were tested on the same 20 fruit photos. Model A got 16 right and Model B got 15 right. How many percentage points more accurate is Model A?
21. 21.🎯 Rohan’s leaf identifier was tested on 20 new leaf photos and got 12 right. What is its accuracy in percent?
22. 22.⚖️ Two recycling sorter models were tested on the same 25 waste photos. Model A got 23 right and Model B got 22 right. How many percentage points more accurate is Model A?
23. 23.🎯 Ananya’s handwriting reader was tested on 4 new handwritten letters and got 4 right. What is its accuracy in percent?
24. 24.❌ Meera’s spam spotter was tested on 16 new messages and got 13 right. How many did it get wrong?
25. 25.📦 Vihaan collected 60 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
26. 26.📋 Planning an AI project: which comes first — recheck every label or train the model? (a) recheck every label (b) train the model
27. 27.📋 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
28. 28.🎯 Diya’s leaf identifier is 90% accurate. If it is tested on 90 new leaf photos, how many should it get right?
29. 29.🎯 Tara’s recycling sorter is 70% accurate. If it is tested on 20 new waste photos, how many should it get right?
30. 30.📋 Planning an AI project: which comes first — fix it and test again or try unseen examples? (a) fix it and test again (b) try unseen examples
31. 31.📦 Ananya collected 90 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
32. 32.📋 Planning an AI project: which comes first — work out the accuracy or label each example? (a) work out the accuracy (b) label each example
33. 33.📋 Planning an AI project: which comes first — recheck every label or gather many examples? (a) recheck every label (b) gather many examples
34. 34.📋 Planning an AI project: which comes first — test on new photos or fix it and test again? (a) test on new photos (b) fix it and test again
35. 35.📋 Planning an AI project: which comes first — collect fruit photos or try unseen examples? (a) collect fruit photos (b) try unseen examples
36. 36.📋 Planning an AI project: which comes first — fix it and test again or set aside test photos? (a) fix it and test again (b) set aside test photos
37. 37.🎯 Saanvi’s fruit sorter is 80% accurate. If it is tested on 90 new fruit photos, how many should it get right?
38. 38.❌ Ishaan’s bird-song app was tested on 27 new bird songs and got 19 right. How many did it get wrong?
39. 39.⚖️ Two leaf identifier models were tested on the same 20 leaf photos. Model A got 17 right and Model B got 11 right. How many percentage points more accurate is Model A?
40. 40.📋 Planning an AI project: which comes first — set aside test photos or label each example? (a) set aside test photos (b) label each example

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