Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 288 · 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 collected 180 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
2. 2.🧪 What is accuracy? (a) total − correct (b) correct + total (c) total × 2 (d) correct ÷ total × 100
3. 3.📋 Planning an AI project: which comes first — gather many examples or work out the accuracy? (a) gather many examples (b) work out the accuracy
4. 4.📋 Planning an AI project: which comes first — work out the accuracy or test on new photos? (a) work out the accuracy (b) test on new photos
5. 5.🎯 Ananya’s spam spotter was tested on 4 new messages and got 3 right. What is its accuracy in percent?
6. 6.🎯 Ishaan’s leaf identifier is 70% accurate. If it is tested on 20 new leaf photos, how many should it get right?
7. 7.📋 Planning an AI project: which comes first — test on new photos or split train and test? (a) test on new photos (b) split train and test
8. 8.🧪 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
9. 9.📋 Planning an AI project: which comes first — fix it and test again or test on new photos? (a) fix it and test again (b) test on new photos
10. 10.📋 Planning an AI project: which comes first — try unseen examples or improve data, retest? (a) try unseen examples (b) improve data, retest
11. 11.❌ Tara’s bird-song app was tested on 27 new bird songs and got 23 right. How many did it get wrong?
12. 12.⚖️ Two fruit sorter models were tested on the same 25 fruit photos. Model A got 24 right and Model B got 21 right. How many percentage points more accurate is Model A?
13. 13.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a new colour screen (b) fewer examples (c) a louder speaker (d) more labelled examples
14. 14.🎯 Saanvi’s leaf identifier is 50% accurate. If it is tested on 30 new leaf photos, how many should it get right?
15. 15.📋 Planning an AI project: which comes first — improve data, retest or test on new photos? (a) improve data, retest (b) test on new photos
16. 16.📋 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
17. 17.📋 Planning an AI project: which comes first — recheck every label or tag each photo by type? (a) recheck every label (b) tag each photo by type
18. 18.📦 Saanvi collected 180 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
19. 19.🎯 Saanvi’s leaf identifier was tested on 4 new leaf photos and got 3 right. What is its accuracy in percent?
20. 20.📋 Planning an AI project: which comes first — choose what to sort or collect fruit photos? (a) choose what to sort (b) collect fruit photos
21. 21.📋 Planning an AI project: which comes first — tag each photo by type or gather many examples? (a) tag each photo by type (b) gather many examples
22. 22.📦 Vihaan collected 40 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
23. 23.📦 Aarav collected 130 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
24. 24.🎯 Kabir’s spam spotter is 70% accurate. If it is tested on 90 new messages, how many should it get right?
25. 25.❌ Nisha’s fruit sorter was tested on 20 new fruit photos and got 15 right. How many did it get wrong?
26. 26.🧪 You can plan a simple AI project with… (a) paper and examples (b) a secret password (c) nothing at all (d) only a big robot
27. 27.📋 Planning an AI project: which comes first — let it learn patterns or set aside test photos? (a) let it learn patterns (b) set aside test photos
28. 28.📋 Planning an AI project: which comes first — work out the accuracy or improve data, retest? (a) work out the accuracy (b) improve data, retest
29. 29.📋 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
30. 30.📋 Planning an AI project: which comes first — calculate percent right or improve data, retest? (a) calculate percent right (b) improve data, retest
31. 31.📋 Planning an AI project: which comes first — let it learn patterns or label each example? (a) let it learn patterns (b) label each example
32. 32.🎯 Meera’s recycling sorter was tested on 5 new waste photos and got 4 right. What is its accuracy in percent?
33. 33.❌ Rohan’s recycling sorter was tested on 22 new waste photos and got 15 right. How many did it get wrong?
34. 34.⚖️ Two leaf identifier models were tested on the same 20 leaf photos. Model A got 19 right and Model B got 16 right. How many percentage points more accurate is Model A?
35. 35.📋 Planning an AI project: which comes first — choose what to sort or improve data, retest? (a) choose what to sort (b) improve data, retest
36. 36.📦 Ishaan collected 100 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
37. 37.🎯 Tara’s spam spotter is 90% accurate. If it is tested on 70 new messages, how many should it get right?
38. 38.🎯 Ananya’s bird-song app was tested on 5 new bird songs and got 4 right. What is its accuracy in percent?
39. 39.📋 Planning an AI project: which comes first — fix it and test again or split train and test? (a) fix it and test again (b) split train and test
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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