Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 51 · 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 label each example? (a) try unseen examples (b) label each example
2. 2.🧪 Why must you test a model on examples it has NOT seen? (a) to see if it learned (b) to make it bigger (c) to make it faster (d) to save paper
3. 3.❌ Tara’s leaf identifier was tested on 18 new leaf photos and got 13 right. How many did it get wrong?
4. 4.📦 Arjun collected 170 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
5. 5.🧪 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
6. 6.📦 Tara collected 40 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
7. 7.⚖️ Two leaf identifier models were tested on the same 50 leaf photos. Model A got 48 right and Model B got 45 right. How many percentage points more accurate is Model A?
8. 8.🧪 What is accuracy? (a) total × 2 (b) correct + total (c) correct ÷ total × 100 (d) total − correct
9. 9.❌ Vihaan’s handwriting reader was tested on 39 new handwritten letters and got 38 right. How many did it get wrong?
10. 10.❌ Diya’s bird-song app was tested on 38 new bird songs and got 37 right. How many did it get wrong?
11. 11.📋 Planning an AI project: which comes first — tag each photo by type or correct bad labels? (a) tag each photo by type (b) correct bad labels
12. 12.📋 Planning an AI project: which comes first — work out the accuracy or improve data, retest? (a) work out the accuracy (b) improve data, retest
13. 13.🎯 Saanvi’s bird-song app was tested on 10 new bird songs and got 8 right. What is its accuracy in percent?
14. 14.❌ Saanvi’s leaf identifier was tested on 35 new leaf photos and got 30 right. How many did it get wrong?
15. 15.🎯 Meera’s leaf identifier is 90% accurate. If it is tested on 20 new leaf photos, how many should it get right?
16. 16.🧪 You can plan a simple AI project with… (a) only a big robot (b) paper and examples (c) nothing at all (d) a secret password
17. 17.🎯 Diya’s recycling sorter is 90% accurate. If it is tested on 30 new waste photos, how many should it get right?
18. 18.⚖️ Two fruit sorter models were tested on the same 50 fruit photos. Model A got 44 right and Model B got 34 right. How many percentage points more accurate is Model A?
19. 19.📦 Nisha collected 150 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
20. 20.⚖️ Two handwriting reader models were tested on the same 10 handwritten letters. Model A got 7 right and Model B got 6 right. How many percentage points more accurate is Model A?
21. 21.⚖️ Two leaf identifier models were tested on the same 25 leaf photos. Model A got 24 right and Model B got 23 right. How many percentage points more accurate is Model A?
22. 22.📋 Planning an AI project: which comes first — correct bad labels or fix it and test again? (a) correct bad labels (b) fix it and test again
23. 23.📋 Planning an AI project: which comes first — label each example or improve data, retest? (a) label each example (b) improve data, retest
24. 24.📋 Planning an AI project: which comes first — improve data, retest or pick a question to solve? (a) improve data, retest (b) pick a question to solve
25. 25.📋 Planning an AI project: which comes first — calculate percent right or train the model? (a) calculate percent right (b) train the model
26. 26.📦 Ishaan collected 60 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
27. 27.⚖️ Two handwriting reader models were tested on the same 10 handwritten letters. Model A got 10 right and Model B got 9 right. How many percentage points more accurate is Model A?
28. 28.📋 Planning an AI project: which comes first — improve data, retest or try unseen examples? (a) improve data, retest (b) try unseen examples
29. 29.📋 Planning an AI project: which comes first — try unseen examples or calculate percent right? (a) try unseen examples (b) calculate percent right
30. 30.❌ Ananya’s fruit sorter was tested on 42 new fruit photos and got 39 right. How many did it get wrong?
31. 31.❌ Meera’s spam spotter was tested on 40 new messages and got 34 right. How many did it get wrong?
32. 32.📦 Diya collected 60 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
33. 33.❌ Kabir’s fruit sorter was tested on 48 new fruit photos and got 39 right. How many did it get wrong?
34. 34.📦 Meera collected 150 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
35. 35.📋 Planning an AI project: which comes first — improve data, retest or test on new photos? (a) improve data, retest (b) test on new photos
36. 36.❌ Nisha’s recycling sorter was tested on 12 new waste photos and got 11 right. How many did it get wrong?
37. 37.🎯 Tara’s leaf identifier was tested on 20 new leaf photos and got 14 right. What is its accuracy in percent?
38. 38.📋 Planning an AI project: which comes first — train the model or label each example? (a) train the model (b) label each example
39. 39.⚖️ Two bird-song app models were tested on the same 25 bird songs. Model A got 19 right and Model B got 13 right. How many percentage points more accurate is Model A?
40. 40.❌ Aarav’s bird-song app was tested on 31 new bird songs and got 25 right. How many did it get wrong?

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