Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 830 · 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.🧪 You can plan a simple AI project with… (a) paper and examples (b) only a big robot (c) nothing at all (d) a secret password
2. 2.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a louder speaker (b) more labelled examples (c) a new colour screen (d) fewer examples
3. 3.📦 Kabir collected 120 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
4. 4.📋 Planning an AI project: which comes first — pick a question to solve or work out the accuracy? (a) pick a question to solve (b) work out the accuracy
5. 5.❌ Tara’s handwriting reader was tested on 41 new handwritten letters and got 40 right. How many did it get wrong?
6. 6.📋 Planning an AI project: which comes first — calculate percent right or let it learn patterns? (a) calculate percent right (b) let it learn patterns
7. 7.🎯 Kabir’s bird-song app was tested on 25 new bird songs and got 13 right. What is its accuracy in percent?
8. 8.📋 Planning an AI project: which comes first — try unseen examples or set aside test photos? (a) try unseen examples (b) set aside test photos
9. 9.❌ Ananya’s handwriting reader was tested on 35 new handwritten letters and got 32 right. How many did it get wrong?
10. 10.📦 Saanvi collected 170 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
11. 11.⚖️ Two spam spotter models were tested on the same 50 messages. Model A got 34 right and Model B got 31 right. How many percentage points more accurate is Model A?
12. 12.🎯 Rohan’s recycling sorter was tested on 50 new waste photos and got 34 right. What is its accuracy in percent?
13. 13.📦 Ishaan collected 70 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
14. 14.🎯 Nisha’s recycling sorter is 90% accurate. If it is tested on 100 new waste photos, how many should it get right?
15. 15.🎯 Vihaan’s leaf identifier is 80% accurate. If it is tested on 90 new leaf photos, how many should it get right?
16. 16.📋 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
17. 17.📦 Meera collected 120 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
18. 18.📋 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
19. 19.📋 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
20. 20.📋 Planning an AI project: which comes first — improve data, retest or choose what to sort? (a) improve data, retest (b) choose what to sort
21. 21.🧪 What is accuracy? (a) correct ÷ total × 100 (b) total − correct (c) correct + total (d) total × 2
22. 22.📦 Tara collected 100 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 — correct bad labels or label each example? (a) correct bad labels (b) label each example
24. 24.📋 Planning an AI project: which comes first — split train and test or try unseen examples? (a) split train and test (b) try unseen examples
25. 25.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 42 right and Model B got 37 right. How many percentage points more accurate is Model A?
26. 26.🧪 Why must you test a model on examples it has NOT seen? (a) to make it bigger (b) to save paper (c) to see if it learned (d) to make it faster
27. 27.📋 Planning an AI project: which comes first — recheck every label or label each example? (a) recheck every label (b) label each example
28. 28.📋 Planning an AI project: which comes first — improve data, retest or correct bad labels? (a) improve data, retest (b) correct bad labels
29. 29.⚖️ Two fruit sorter models were tested on the same 10 fruit photos. Model A got 8 right and Model B got 7 right. How many percentage points more accurate is Model A?
30. 30.🎯 Ishaan’s spam spotter is 70% accurate. If it is tested on 30 new messages, how many should it get right?
31. 31.📋 Planning an AI project: which comes first — calculate percent right or gather many examples? (a) calculate percent right (b) gather many examples
32. 32.🎯 Kabir’s bird-song app is 70% accurate. If it is tested on 10 new bird songs, how many should it get right?
33. 33.📋 Planning an AI project: which comes first — work out the accuracy or try unseen examples? (a) work out the accuracy (b) try unseen examples
34. 34.⚖️ Two recycling sorter models were tested on the same 25 waste photos. Model A got 24 right and Model B got 23 right. How many percentage points more accurate is Model A?
35. 35.📦 Vihaan collected 130 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
36. 36.❌ Rohan’s leaf identifier was tested on 14 new leaf photos and got 10 right. How many did it get wrong?
37. 37.🎯 Ananya’s bird-song app is 70% accurate. If it is tested on 60 new bird songs, how many should it get right?
38. 38.🎯 Saanvi’s handwriting reader is 90% accurate. If it is tested on 30 new handwritten letters, how many should it get right?
39. 39.🎯 Rohan’s fruit sorter was tested on 5 new fruit photos and got 4 right. What is its accuracy in percent?
40. 40.📦 Ananya collected 150 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?

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