Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 954 · 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.❌ Vihaan’s leaf identifier was tested on 32 new leaf photos and got 25 right. How many did it get wrong?
2. 2.❌ Aarav’s spam spotter was tested on 48 new messages and got 39 right. How many did it get wrong?
3. 3.🎯 Tara’s fruit sorter was tested on 20 new fruit photos and got 17 right. What is its accuracy in percent?
4. 4.❌ Arjun’s handwriting reader was tested on 23 new handwritten letters and got 16 right. How many did it get wrong?
5. 5.📋 Planning an AI project: which comes first — try unseen examples or train the model? (a) try unseen examples (b) train the model
6. 6.🎯 Meera’s fruit sorter is 80% accurate. If it is tested on 40 new fruit photos, how many should it get right?
7. 7.⚖️ Two bird-song app models were tested on the same 10 bird songs. Model A got 10 right and Model B got 9 right. How many percentage points more accurate is Model A?
8. 8.📦 Kabir collected 160 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
9. 9.🧪 What is accuracy? (a) correct ÷ total × 100 (b) correct + total (c) total − correct (d) total × 2
10. 10.📋 Planning an AI project: which comes first — try unseen examples or set aside test photos? (a) try unseen examples (b) set aside test photos
11. 11.⚖️ Two spam spotter models were tested on the same 20 messages. Model A got 20 right and Model B got 19 right. How many percentage points more accurate is Model A?
12. 12.🧪 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
13. 13.⚖️ Two recycling sorter models were tested on the same 50 waste photos. Model A got 46 right and Model B got 41 right. How many percentage points more accurate is Model A?
14. 14.⚖️ Two handwriting reader models were tested on the same 25 handwritten letters. Model A got 24 right and Model B got 19 right. How many percentage points more accurate is Model A?
15. 15.📋 Planning an AI project: which comes first — calculate percent right or test on new photos? (a) calculate percent right (b) test on new photos
16. 16.📋 Planning an AI project: which comes first — recheck every label or tag each photo by type? (a) recheck every label (b) tag each photo by type
17. 17.📦 Aarav collected 180 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
18. 18.⚖️ Two handwriting reader models were tested on the same 50 handwritten letters. Model A got 46 right and Model B got 31 right. How many percentage points more accurate is Model A?
19. 19.❌ Rohan’s bird-song app was tested on 25 new bird songs and got 19 right. How many did it get wrong?
20. 20.🎯 Rohan’s recycling sorter was tested on 4 new waste photos and got 4 right. What is its accuracy in percent?
21. 21.📦 Aarav collected 50 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
22. 22.🎯 Rohan’s fruit sorter was tested on 25 new fruit photos and got 15 right. What is its accuracy in percent?
23. 23.📋 Planning an AI project: which comes first — try unseen examples or recheck every label? (a) try unseen examples (b) recheck every label
24. 24.🎯 Ananya’s spam spotter is 90% accurate. If it is tested on 100 new messages, how many should it get right?
25. 25.🧪 Your fruit sorter mixes up oranges and lemons. What helps most? (a) more labelled examples (b) fewer examples (c) a new colour screen (d) a louder speaker
26. 26.📦 Aarav collected 50 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
27. 27.❌ Ishaan’s bird-song app was tested on 42 new bird songs and got 34 right. How many did it get wrong?
28. 28.📋 Planning an AI project: which comes first — collect fruit photos or try unseen examples? (a) collect fruit photos (b) try unseen examples
29. 29.📋 Planning an AI project: which comes first — label each example or set aside test photos? (a) label each example (b) set aside test photos
30. 30.⚖️ Two fruit sorter models were tested on the same 10 fruit photos. Model A got 6 right and Model B got 5 right. How many percentage points more accurate is Model A?
31. 31.❌ Diya’s leaf identifier was tested on 21 new leaf photos and got 14 right. How many did it get wrong?
32. 32.🎯 Aarav’s bird-song app was tested on 20 new bird songs and got 19 right. What is its accuracy in percent?
33. 33.📦 Diya collected 110 labelled leaf photos for a leaf identifier and keeps one fifth of them for testing. How many are left for training?
34. 34.🎯 Ananya’s recycling sorter was tested on 50 new waste photos and got 33 right. What is its accuracy in percent?
35. 35.📋 Planning an AI project: which comes first — collect fruit photos or tag each photo by type? (a) collect fruit photos (b) tag each photo by type
36. 36.⚖️ Two fruit sorter models were tested on the same 25 fruit photos. Model A got 23 right and Model B got 17 right. How many percentage points more accurate is Model A?
37. 37.🧪 You can plan a simple AI project with… (a) nothing at all (b) only a big robot (c) a secret password (d) paper and examples
38. 38.📋 Planning an AI project: which comes first — gather many examples or set aside test photos? (a) gather many examples (b) set aside test photos
39. 39.🎯 Vihaan’s fruit sorter was tested on 25 new fruit photos and got 14 right. What is its accuracy in percent?
40. 40.📋 Planning an AI project: which comes first — label each example or correct bad labels? (a) label each example (b) correct bad labels

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