Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 572 · 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 calculate percent right? (a) try unseen examples (b) calculate percent right
2. 2.📦 Saanvi collected 150 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
3. 3.🎯 Diya’s fruit sorter is 90% accurate. If it is tested on 80 new fruit photos, how many should it get right?
4. 4.❌ Kabir’s bird-song app was tested on 22 new bird songs and got 14 right. How many did it get wrong?
5. 5.📦 Kabir collected 100 labelled bird songs for a bird-song app and keeps one fifth of them for testing. How many are left for training?
6. 6.📋 Planning an AI project: which comes first — work out the accuracy or set aside test photos? (a) work out the accuracy (b) set aside test photos
7. 7.📋 Planning an AI project: which comes first — try unseen examples or set aside test photos? (a) try unseen examples (b) set aside test photos
8. 8.🧪 What is accuracy? (a) correct + total (b) total × 2 (c) correct ÷ total × 100 (d) total − correct
9. 9.📋 Planning an AI project: which comes first — work out the accuracy or improve data, retest? (a) work out the accuracy (b) improve data, retest
10. 10.📋 Planning an AI project: which comes first — choose what to sort or label each example? (a) choose what to sort (b) label each example
11. 11.📋 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
12. 12.🧪 Why must you test a model on examples it has NOT seen? (a) to make it bigger (b) to save paper (c) to make it faster (d) to see if it learned
13. 13.❌ Nisha’s bird-song app was tested on 42 new bird songs and got 40 right. How many did it get wrong?
14. 14.📋 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
15. 15.🎯 Diya’s recycling sorter was tested on 25 new waste photos and got 23 right. What is its accuracy in percent?
16. 16.⚖️ Two fruit sorter models were tested on the same 20 fruit photos. Model A got 17 right and Model B got 13 right. How many percentage points more accurate is Model A?
17. 17.❌ Ananya’s leaf identifier was tested on 49 new leaf photos and got 46 right. How many did it get wrong?
18. 18.📋 Planning an AI project: which comes first — label each example or set aside test photos? (a) label each example (b) set aside test photos
19. 19.📋 Planning an AI project: which comes first — test on new photos or tag each photo by type? (a) test on new photos (b) tag each photo by type
20. 20.🎯 Kabir’s recycling sorter is 70% accurate. If it is tested on 90 new waste photos, how many should it get right?
21. 21.⚖️ Two handwriting reader models were tested on the same 20 handwritten letters. Model A got 17 right and Model B got 10 right. How many percentage points more accurate is Model A?
22. 22.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) a new colour screen (b) fewer examples (c) more labelled examples (d) a louder speaker
23. 23.📦 Arjun collected 90 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
24. 24.📦 Saanvi collected 140 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
25. 25.🎯 Aarav’s handwriting reader is 90% accurate. If it is tested on 10 new handwritten letters, how many should it get right?
26. 26.🎯 Kabir’s leaf identifier was tested on 20 new leaf photos and got 18 right. What is its accuracy in percent?
27. 27.📋 Planning an AI project: which comes first — correct bad labels or pick a question to solve? (a) correct bad labels (b) pick a question to solve
28. 28.📦 Kabir collected 200 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
29. 29.🎯 Kabir’s leaf identifier is 50% accurate. If it is tested on 20 new leaf photos, how many should it get right?
30. 30.🎯 Meera’s recycling sorter was tested on 20 new waste photos and got 15 right. What is its accuracy in percent?
31. 31.⚖️ 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?
32. 32.📋 Planning an AI project: which comes first — collect fruit photos or recheck every label? (a) collect fruit photos (b) recheck every label
33. 33.⚖️ Two fruit sorter models were tested on the same 20 fruit photos. Model A got 12 right and Model B got 10 right. How many percentage points more accurate is Model A?
34. 34.❌ Diya’s leaf identifier was tested on 18 new leaf photos and got 13 right. How many did it get wrong?
35. 35.🎯 Nisha’s bird-song app was tested on 50 new bird songs and got 43 right. What is its accuracy in percent?
36. 36.🎯 Rohan’s handwriting reader was tested on 10 new handwritten letters and got 5 right. What is its accuracy in percent?
37. 37.📋 Planning an AI project: which comes first — test on new photos or train the model? (a) test on new photos (b) train the model
38. 38.⚖️ Two leaf identifier models were tested on the same 20 leaf photos. Model A got 18 right and Model B got 14 right. How many percentage points more accurate is Model A?
39. 39.📦 Tara collected 140 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
40. 40.📋 Planning an AI project: which comes first — improve data, retest or train the model? (a) improve data, retest (b) train the model

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