Build Your Own AI Project Worksheet

AI for students lesson 23: Design a tiny AI project · Set 500 · 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 — recheck every label or fix it and test again? (a) recheck every label (b) fix it and test again
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.📋 Planning an AI project: which comes first — test on new photos or calculate percent right? (a) test on new photos (b) calculate percent right
4. 4.📋 Planning an AI project: which comes first — work out the accuracy or label each example? (a) work out the accuracy (b) label each example
5. 5.🎯 Kabir’s handwriting reader was tested on 4 new handwritten letters and got 4 right. What is its accuracy in percent?
6. 6.🎯 Tara’s spam spotter is 60% accurate. If it is tested on 40 new messages, how many should it get right?
7. 7.🎯 Arjun’s leaf identifier was tested on 20 new leaf photos and got 17 right. What is its accuracy in percent?
8. 8.🧪 You can plan a simple AI project with… (a) a secret password (b) nothing at all (c) paper and examples (d) only a big robot
9. 9.❌ Diya’s leaf identifier was tested on 39 new leaf photos and got 30 right. How many did it get wrong?
10. 10.🎯 Rohan’s leaf identifier is 60% accurate. If it is tested on 50 new leaf photos, how many should it get right?
11. 11.🎯 Kabir’s recycling sorter was tested on 5 new waste photos and got 4 right. What is its accuracy in percent?
12. 12.🧪 What is accuracy? (a) total − correct (b) correct + total (c) total × 2 (d) correct ÷ total × 100
13. 13.🎯 Saanvi’s fruit sorter was tested on 5 new fruit photos and got 5 right. What is its accuracy in percent?
14. 14.📦 Diya collected 40 labelled fruit photos for a fruit sorter and keeps one fifth of them for testing. How many are left for training?
15. 15.🎯 Diya’s spam spotter is 80% accurate. If it is tested on 20 new messages, how many should it get right?
16. 16.📦 Kabir collected 150 labelled messages for a spam spotter and keeps one fifth of them for testing. How many are left for training?
17. 17.📋 Planning an AI project: which comes first — recheck every label or label each example? (a) recheck every label (b) label each example
18. 18.⚖️ Two recycling sorter models were tested on the same 10 waste photos. Model A got 9 right and Model B got 8 right. How many percentage points more accurate is Model A?
19. 19.⚖️ Two fruit sorter models were tested on the same 25 fruit photos. Model A got 18 right and Model B got 14 right. How many percentage points more accurate is Model A?
20. 20.📋 Planning an AI project: which comes first — train the model or tag each photo by type? (a) train the model (b) tag each photo by type
21. 21.⚖️ Two recycling sorter models were tested on the same 20 waste photos. Model A got 14 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) a louder speaker (d) more labelled examples
23. 23.📦 Kabir collected 80 labelled waste photos for a recycling sorter and keeps one fifth of them for testing. How many are left for training?
24. 24.📋 Planning an AI project: which comes first — choose what to sort or gather many examples? (a) choose what to sort (b) gather many examples
25. 25.⚖️ Two handwriting reader models were tested on the same 10 handwritten letters. Model A got 10 right and Model B got 7 right. How many percentage points more accurate is Model A?
26. 26.⚖️ Two spam spotter models were tested on the same 20 messages. Model A got 13 right and Model B got 12 right. How many percentage points more accurate is Model A?
27. 27.📋 Planning an AI project: which comes first — gather many examples or work out the accuracy? (a) gather many examples (b) work out the accuracy
28. 28.🧪 Why must you test a model on examples it has NOT seen? (a) to see if it learned (b) to make it faster (c) to make it bigger (d) to save paper
29. 29.🎯 Vihaan’s fruit sorter was tested on 25 new fruit photos and got 21 right. What is its accuracy in percent?
30. 30.📋 Planning an AI project: which comes first — improve data, retest or test on new photos? (a) improve data, retest (b) test on new photos
31. 31.⚖️ Two spam spotter models were tested on the same 20 messages. Model A got 14 right and Model B got 13 right. How many percentage points more accurate is Model A?
32. 32.🎯 Diya’s fruit sorter is 60% accurate. If it is tested on 10 new fruit photos, how many should it get right?
33. 33.❌ Meera’s bird-song app was tested on 37 new bird songs and got 34 right. How many did it get wrong?
34. 34.🎯 Nisha’s recycling sorter was tested on 50 new waste photos and got 43 right. What is its accuracy in percent?
35. 35.📋 Planning an AI project: which comes first — calculate percent right or try unseen examples? (a) calculate percent right (b) try unseen examples
36. 36.📦 Saanvi collected 150 labelled handwritten letters for a handwriting reader and keeps one fifth of them for testing. How many are left for training?
37. 37.🎯 Diya’s leaf identifier is 50% accurate. If it is tested on 80 new leaf photos, how many should it get right?
38. 38.📋 Planning an AI project: which comes first — try unseen examples or pick a question to solve? (a) try unseen examples (b) pick a question to solve
39. 39.📋 Planning an AI project: which comes first — choose what to sort or train the model? (a) choose what to sort (b) train the model
40. 40.❌ Diya’s fruit sorter was tested on 27 new fruit photos and got 21 right. How many did it get wrong?

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