Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 851 · 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.📋 Planning an AI project: which comes first — try unseen examples or tag each photo by type? (a) try unseen examples (b) tag each photo by type
2. 2.🎯 Rohan’s spam spotter is 90% accurate. If it is tested on 30 new messages, how many should it get right?
3. 3.📦 Saanvi collected 70 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
4. 4.📋 Planning an AI project: which comes first — train the model or pick a question to solve? (a) train the model (b) pick a question to solve
5. 5.📋 Planning an AI project: which comes first — try unseen examples or set aside test photos? (a) try unseen examples (b) set aside test photos
6. 6.🎯 Tara’s handwriting reader was tested on 4 new handwritten letters and got 2 right. What is its accuracy in percent?
7. 7.📦 Rohan collected 70 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
8. 8.🧪 You can plan a simple AI project with… (a) a secret password (b) paper and examples (c) nothing at all (d) only a big robot
9. 9.🧪 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
10. 10.🎯 Aarav’s bird-song app is 50% accurate. If it is tested on 70 new bird songs, how many should it get right?
11. 11.📋 Planning an AI project: which comes first — choose what to sort or recheck every label? (a) choose what to sort (b) recheck every label
12. 12.📋 Planning an AI project: which comes first — try unseen examples or improve data, retest? (a) try unseen examples (b) improve data, retest
13. 13.📋 Planning an AI project: which comes first — improve data, retest or work out the accuracy? (a) improve data, retest (b) work out the accuracy
14. 14.🎯 Vihaan’s spam spotter is 80% accurate. If it is tested on 20 new messages, how many should it get right?
15. 15.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) more labelled examples (b) a louder speaker (c) a new colour screen (d) fewer examples
16. 16.📋 Planning an AI project: which comes first — recheck every label or work out the accuracy? (a) recheck every label (b) work out the accuracy
17. 17.📋 Planning an AI project: which comes first — calculate percent right or test on new photos? (a) calculate percent right (b) test on new photos
18. 18.⚖️ Two fruit sorter models were tested on the same 25 fruit photos. Model A got 21 right and Model B got 14 right. How many percentage points more accurate is Model A?
19. 19.🎯 Aarav’s leaf identifier was tested on 4 new leaf photos and got 2 right. What is its accuracy in percent?
20. 20.❌ Nisha’s spam spotter was tested on 44 new messages and got 36 right. How many did it get wrong?
21. 21.🎯 Nisha’s leaf identifier is 70% accurate. If it is tested on 80 new leaf photos, how many should it get right?
22. 22.📋 Planning an AI project: which comes first — tag each photo by type or collect fruit photos? (a) tag each photo by type (b) collect fruit photos
23. 23.⚖️ Two spam spotter models were tested on the same 10 messages. Model A got 10 right and Model B got 9 right. How many percentage points more accurate is Model A?
24. 24.❌ Kabir’s recycling sorter was tested on 40 new waste photos and got 31 right. How many did it get wrong?
25. 25.⚖️ Two recycling sorter models were tested on the same 20 waste photos. Model A got 19 right and Model B got 10 right. How many percentage points more accurate is Model A?
26. 26.📋 Planning an AI project: which comes first — gather many examples or fix it and test again? (a) gather many examples (b) fix it and test again
27. 27.📋 Planning an AI project: which comes first — label each example or pick a question to solve? (a) label each example (b) pick a question to solve
28. 28.📋 Planning an AI project: which comes first — choose what to sort or label each example? (a) choose what to sort (b) label each example
29. 29.⚖️ Two bird-song app models were tested on the same 20 bird songs. Model A got 17 right and Model B got 12 right. How many percentage points more accurate is Model A?
30. 30.🎯 Tara’s spam spotter is 50% accurate. If it is tested on 50 new messages, how many should it get right?
31. 31.📦 Ananya collected 100 labelled handwritten letters for a handwriting reader 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 gather many examples? (a) work out the accuracy (b) gather many examples
33. 33.🧪 What is accuracy? (a) correct ÷ total × 100 (b) total × 2 (c) total − correct (d) correct + total
34. 34.🎯 Saanvi’s bird-song app was tested on 5 new bird songs and got 3 right. What is its accuracy in percent?
35. 35.📦 Nisha collected 60 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
36. 36.📋 Planning an AI project: which comes first — train the model or set aside test photos? (a) train the model (b) set aside test photos
37. 37.📦 Aarav collected 70 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
38. 38.🎯 Saanvi’s bird-song app is 60% accurate. If it is tested on 10 new bird songs, how many should it get right?
39. 39.📦 Saanvi collected 140 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
40. 40.🎯 Saanvi’s spam spotter is 50% accurate. If it is tested on 30 new messages, how many should it get right?

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