Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 513 · 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 — set aside test photos or let it learn patterns? (a) set aside test photos (b) let it learn patterns
2. 2.📋 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
3. 3.🧪 What is accuracy? (a) correct + total (b) total × 2 (c) correct ÷ total × 100 (d) total − correct
4. 4.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a louder speaker (b) fewer examples (c) a new colour screen (d) more labelled examples
5. 5.🎯 Aarav’s leaf identifier was tested on 4 new leaf photos and got 3 right. What is its accuracy in percent?
6. 6.⚖️ Two fruit sorter models were tested on the same 10 fruit photos. Model A got 8 right and Model B got 5 right. How many percentage points more accurate is Model A?
7. 7.📋 Planning an AI project: which comes first — train the model or set aside test photos? (a) train the model (b) set aside test photos
8. 8.📋 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
9. 9.❌ Aarav’s spam spotter was tested on 28 new messages and got 22 right. How many did it get wrong?
10. 10.🧪 Why must you test a model on examples it has NOT seen? (a) to make it faster (b) to save paper (c) to see if it learned (d) to make it bigger
11. 11.📋 Planning an AI project: which comes first — gather many examples or correct bad labels? (a) gather many examples (b) correct bad labels
12. 12.🎯 Arjun’s recycling sorter is 50% accurate. If it is tested on 60 new waste photos, how many should it get right?
13. 13.🎯 Kabir’s fruit sorter is 70% accurate. If it is tested on 10 new fruit photos, how many should it get right?
14. 14.⚖️ 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?
15. 15.📦 Arjun collected 170 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
16. 16.🧪 You can plan a simple AI project with… (a) only a big robot (b) a secret password (c) nothing at all (d) paper and examples
17. 17.⚖️ Two spam spotter models were tested on the same 25 messages. Model A got 25 right and Model B got 20 right. How many percentage points more accurate is Model A?
18. 18.🎯 Aarav’s recycling sorter was tested on 4 new waste photos and got 4 right. What is its accuracy in percent?
19. 19.📋 Planning an AI project: which comes first — recheck every label or set aside test photos? (a) recheck every label (b) set aside test photos
20. 20.📋 Planning an AI project: which comes first — work out the accuracy or train the model? (a) work out the accuracy (b) train the model
21. 21.📦 Saanvi collected 80 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
22. 22.📋 Planning an AI project: which comes first — try unseen examples or improve data, retest? (a) try unseen examples (b) improve data, retest
23. 23.📋 Planning an AI project: which comes first — calculate percent right or improve data, retest? (a) calculate percent right (b) improve data, retest
24. 24.🎯 Saanvi’s fruit sorter is 50% accurate. If it is tested on 40 new fruit photos, how many should it get right?
25. 25.❌ Tara’s leaf identifier was tested on 28 new leaf photos and got 19 right. How many did it get wrong?
26. 26.📋 Planning an AI project: which comes first — pick a question to solve or test on new photos? (a) pick a question to solve (b) test on new photos
27. 27.📋 Planning an AI project: which comes first — try unseen examples or work out the accuracy? (a) try unseen examples (b) work out the accuracy
28. 28.📋 Planning an AI project: which comes first — train the model or gather many examples? (a) train the model (b) gather many examples
29. 29.❌ Kabir’s handwriting reader was tested on 11 new handwritten letters and got 10 right. How many did it get wrong?
30. 30.📋 Planning an AI project: which comes first — collect fruit photos or label each example? (a) collect fruit photos (b) label each example
31. 31.📋 Planning an AI project: which comes first — improve data, retest or test on new photos? (a) improve data, retest (b) test on new photos
32. 32.📋 Planning an AI project: which comes first — gather many examples or tag each photo by type? (a) gather many examples (b) tag each photo by type
33. 33.❌ Aarav’s leaf identifier was tested on 32 new leaf photos and got 28 right. How many did it get wrong?
34. 34.🎯 Tara’s fruit sorter is 50% accurate. If it is tested on 20 new fruit photos, how many should it get right?
35. 35.📦 Vihaan collected 60 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
36. 36.🎯 Aarav’s leaf identifier is 90% accurate. If it is tested on 80 new leaf photos, how many should it get right?
37. 37.📋 Planning an AI project: which comes first — label each example or let it learn patterns? (a) label each example (b) let it learn patterns
38. 38.📋 Planning an AI project: which comes first — set aside test photos or label each example? (a) set aside test photos (b) label each example
39. 39.📋 Planning an AI project: which comes first — test on new photos or train the model? (a) test on new photos (b) train the model
40. 40.📦 Ishaan collected 160 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?

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