Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 517 · 40 questions

AI tip (Show, label, learn): Machine learning learns patterns from many labelled examples — a label is the right answer tag on each example. Good for training: many varied examples, correct labels, every type the model will meet, and separate new data for testing. Bad for training: very few examples, wrong or random labels, only one kind of example, blurry examples, testing on the training data.

1. 1.Which of these is bad for training a model? (a) testing on training data (b) many varied examples (c) new data for testing (d) photos in many lights
2. 2.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) testing on training data (d) photos in many lights
3. 3.Which of these is bad for training a model? (a) wrong labels (b) checking labels twice (c) many varied examples (d) photos in many lights
4. 4.True or false: “examples of every type” is good for training a model. (a) True (b) False
5. 5.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
6. 6.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) copies of one photo (d) photos in many lights
7. 7.🔍 Which is the odd one out? (a) photos in many lights (b) labels added at random (c) very few examples (d) blurry, unclear photos
8. 8.Which of these is bad for training a model? (a) labels added at random (b) many varied examples (c) correct labels (d) photos in many lights
9. 9.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) wrong labels (d) correct labels
10. 10.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
11. 11.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything
12. 12.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
13. 13.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
14. 14.True or false: “many varied examples” is good for training a model. (a) True (b) False
15. 15.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) blurry, unclear photos (d) examples of every type
16. 16.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) labels added at random (d) examples of every type
17. 17.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
18. 18.True or false: “many varied examples” is bad for training a model. (a) True (b) False
19. 19.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) wrong labels (d) correct labels
20. 20.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) photos in many lights (d) very few examples
21. 21.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
22. 22.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
24. 24.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) many varied examples (d) very few examples
25. 25.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
26. 26.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
27. 27.Which of these is bad for training a model? (a) only one kind of example (b) many varied examples (c) photos in many lights (d) new data for testing
28. 28.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
29. 29.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) many varied examples (d) only one kind of example
30. 30.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
31. 31.Which of these is bad for training a model? (a) clear, sharp examples (b) copies of one photo (c) correct labels (d) many varied examples
32. 32.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
33. 33.Which of these is bad for training a model? (a) blurry, unclear photos (b) many varied examples (c) correct labels (d) new data for testing
34. 34.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
36. 36.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
37. 37.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
39. 39.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
40. 40.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False

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