Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 392 · 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 — gather many examples or work out the accuracy? (a) gather many examples (b) work out the accuracy
2. 2.📋 Planning an AI project: which comes first — set aside test photos or let it learn patterns? (a) set aside test photos (b) let it learn patterns
3. 3.❌ Vihaan’s bird-song app was tested on 35 new bird songs and got 26 right. How many did it get wrong?
4. 4.🎯 Arjun’s fruit sorter is 80% accurate. If it is tested on 60 new fruit photos, how many should it get right?
5. 5.📦 Kabir collected 90 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
6. 6.📋 Planning an AI project: which comes first — tag each photo by type or test on new photos? (a) tag each photo by type (b) test on new photos
7. 7.🎯 Nisha’s recycling sorter is 60% accurate. If it is tested on 40 new waste photos, how many should it get right?
8. 8.⚖️ Two fruit sorter models were tested on the same 25 fruit photos. Model A got 19 right and Model B got 17 right. How many percentage points more accurate is Model A?
9. 9.❌ Saanvi’s spam spotter was tested on 18 new messages and got 10 right. How many did it get wrong?
10. 10.📋 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
11. 11.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 49 right and Model B got 40 right. How many percentage points more accurate is Model A?
12. 12.📋 Planning an AI project: which comes first — let it learn patterns or choose what to sort? (a) let it learn patterns (b) choose what to sort
13. 13.🧪 Why must you test a model on examples it has NOT seen? (a) to make it faster (b) to make it bigger (c) to save paper (d) to see if it learned
14. 14.🎯 Aarav’s bird-song app was tested on 10 new bird songs and got 9 right. What is its accuracy in percent?
15. 15.📦 Ananya collected 70 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
16. 16.📦 Arjun collected 50 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
17. 17.🧪 You can plan a simple AI project with… (a) nothing at all (b) a secret password (c) paper and examples (d) only a big robot
18. 18.🎯 Kabir’s spam spotter is 90% accurate. If it is tested on 70 new messages, how many should it get right?
19. 19.⚖️ Two handwriting reader models were tested on the same 25 handwritten letters. Model A got 25 right and Model B got 20 right. How many percentage points more accurate is Model A?
20. 20.🎯 Kabir’s recycling sorter was tested on 5 new waste photos and got 3 right. What is its accuracy in percent?
21. 21.📋 Planning an AI project: which comes first — calculate percent right or test on new photos? (a) calculate percent right (b) test on new photos
22. 22.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) more labelled examples (b) fewer examples (c) a louder speaker (d) a new colour screen
23. 23.📋 Planning an AI project: which comes first — correct bad labels or choose what to sort? (a) correct bad labels (b) choose what to sort
24. 24.📋 Planning an AI project: which comes first — correct bad labels or set aside test photos? (a) correct bad labels (b) set aside test photos
25. 25.📋 Planning an AI project: which comes first — collect fruit photos or let it learn patterns? (a) collect fruit photos (b) let it learn patterns
26. 26.🎯 Saanvi’s leaf identifier is 60% accurate. If it is tested on 80 new leaf photos, how many should it get right?
27. 27.⚖️ Two handwriting reader models were tested on the same 20 handwritten letters. Model A got 14 right and Model B got 12 right. How many percentage points more accurate is Model A?
28. 28.🧪 What is accuracy? (a) correct ÷ total × 100 (b) correct + total (c) total × 2 (d) total − correct
29. 29.📋 Planning an AI project: which comes first — improve data, retest or let it learn patterns? (a) improve data, retest (b) let it learn patterns
30. 30.🎯 Kabir’s bird-song app is 80% accurate. If it is tested on 70 new bird songs, how many should it get right?
31. 31.📋 Planning an AI project: which comes first — label each example or work out the accuracy? (a) label each example (b) work out the accuracy
32. 32.🎯 Ananya’s spam spotter was tested on 50 new messages and got 33 right. What is its accuracy in percent?
33. 33.🎯 Diya’s spam spotter was tested on 20 new messages and got 10 right. What is its accuracy in percent?
34. 34.⚖️ Two recycling sorter models were tested on the same 10 waste photos. Model A got 10 right and Model B got 6 right. How many percentage points more accurate is Model A?
35. 35.📦 Nisha collected 190 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
36. 36.📋 Planning an AI project: which comes first — choose what to sort or correct bad labels? (a) choose what to sort (b) correct bad labels
37. 37.📋 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
38. 38.🎯 Meera’s recycling sorter was tested on 50 new waste photos and got 42 right. What is its accuracy in percent?
39. 39.🎯 Vihaan’s spam spotter was tested on 10 new messages and got 6 right. What is its accuracy in percent?
40. 40.❌ Ishaan’s handwriting reader was tested on 16 new handwritten letters and got 14 right. How many did it get wrong?

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