Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 494 · 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.❌ Nisha’s fruit sorter was tested on 33 new fruit photos and got 28 right. How many did it get wrong?
2. 2.📦 Ishaan collected 60 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
3. 3.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a new colour screen (b) more labelled examples (c) fewer examples (d) a louder speaker
4. 4.📋 Planning an AI project: which comes first — calculate percent right or recheck every label? (a) calculate percent right (b) recheck every label
5. 5.🎯 Saanvi’s bird-song app is 60% accurate. If it is tested on 70 new bird songs, how many should it get right?
6. 6.📋 Planning an AI project: which comes first — fix it and test again or train the model? (a) fix it and test again (b) train the model
7. 7.🎯 Tara’s spam spotter was tested on 20 new messages and got 11 right. What is its accuracy in percent?
8. 8.🧪 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
9. 9.❌ Saanvi’s fruit sorter was tested on 30 new fruit photos and got 27 right. How many did it get wrong?
10. 10.🧪 You can plan a simple AI project with… (a) a secret password (b) only a big robot (c) paper and examples (d) nothing at all
11. 11.📋 Planning an AI project: which comes first — correct bad labels or work out the accuracy? (a) correct bad labels (b) work out the accuracy
12. 12.📋 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
13. 13.🧪 What is accuracy? (a) total − correct (b) total × 2 (c) correct ÷ total × 100 (d) correct + total
14. 14.📦 Arjun collected 40 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
15. 15.🎯 Tara’s fruit sorter is 80% accurate. If it is tested on 40 new fruit photos, how many should it get right?
16. 16.📋 Planning an AI project: which comes first — label each example or try unseen examples? (a) label each example (b) try unseen examples
17. 17.🎯 Kabir’s recycling sorter is 90% accurate. If it is tested on 20 new waste photos, how many should it get right?
18. 18.📦 Diya collected 170 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
19. 19.📋 Planning an AI project: which comes first — improve data, retest or gather many examples? (a) improve data, retest (b) gather many examples
20. 20.📋 Planning an AI project: which comes first — choose what to sort or let it learn patterns? (a) choose what to sort (b) let it learn patterns
21. 21.🎯 Ananya’s leaf identifier was tested on 10 new leaf photos and got 8 right. What is its accuracy in percent?
22. 22.📋 Planning an AI project: which comes first — set aside test photos or calculate percent right? (a) set aside test photos (b) calculate percent right
23. 23.⚖️ Two recycling sorter models were tested on the same 50 waste photos. Model A got 44 right and Model B got 25 right. How many percentage points more accurate is Model A?
24. 24.❌ Diya’s fruit sorter was tested on 16 new fruit photos and got 13 right. How many did it get wrong?
25. 25.🎯 Diya’s recycling sorter is 50% accurate. If it is tested on 80 new waste photos, how many should it get right?
26. 26.🎯 Kabir’s handwriting reader was tested on 10 new handwritten letters and got 8 right. What is its accuracy in percent?
27. 27.❌ Nisha’s spam spotter was tested on 48 new messages and got 42 right. How many did it get wrong?
28. 28.📦 Nisha collected 80 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
29. 29.⚖️ Two fruit sorter models were tested on the same 10 fruit photos. Model A got 9 right and Model B got 8 right. How many percentage points more accurate is Model A?
30. 30.📋 Planning an AI project: which comes first — choose what to sort or gather many examples? (a) choose what to sort (b) gather many examples
31. 31.📋 Planning an AI project: which comes first — let it learn patterns or test on new photos? (a) let it learn patterns (b) test on new photos
32. 32.❌ Saanvi’s spam spotter was tested on 24 new messages and got 15 right. How many did it get wrong?
33. 33.🎯 Nisha’s fruit sorter is 50% accurate. If it is tested on 20 new fruit photos, how many should it get right?
34. 34.⚖️ Two recycling sorter models were tested on the same 50 waste photos. Model A got 37 right and Model B got 33 right. How many percentage points more accurate is Model A?
35. 35.⚖️ Two leaf identifier models were tested on the same 10 leaf photos. Model A got 9 right and Model B got 7 right. How many percentage points more accurate is Model A?
36. 36.📋 Planning an AI project: which comes first — calculate percent right or label each example? (a) calculate percent right (b) label each example
37. 37.⚖️ Two handwriting reader models were tested on the same 20 handwritten letters. Model A got 20 right and Model B got 17 right. How many percentage points more accurate is Model A?
38. 38.📋 Planning an AI project: which comes first — try unseen examples or recheck every label? (a) try unseen examples (b) recheck every label
39. 39.📦 Rohan collected 140 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
40. 40.📋 Planning an AI project: which comes first — calculate percent right or pick a question to solve? (a) calculate percent right (b) pick a question to solve

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