Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 394 · 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.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
2. 2.📚 Usually, more good examples make a model… (a) better at its task (b) slower to switch on (c) change colour (d) forget everything
3. 3.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
4. 4.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
5. 5.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
6. 6.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) testing on training data (d) blurry, unclear photos
7. 7.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) testing on training data (d) new data for testing
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
9. 9.Which of these is bad for training a model? (a) examples of every type (b) blurry, unclear photos (c) photos in many lights (d) checking labels twice
10. 10.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
12. 12.Which of these is bad for training a model? (a) correct labels (b) labels added at random (c) photos in many lights (d) examples of every type
13. 13.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
14. 14.🔍 Which is the odd one out? (a) new data for testing (b) copies of one photo (c) photos in many lights (d) many varied examples
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
16. 16.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
17. 17.Which of these is good for training a model? (a) copies of one photo (b) correct labels (c) very few examples (d) blurry, unclear photos
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
19. 19.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) the price (d) a sticker on a laptop
20. 20.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
21. 21.True or false: “wrong labels” is bad for training a model. (a) True (b) False
22. 22.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
24. 24.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
25. 25.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
26. 26.Which of these is good for training a model? (a) many varied examples (b) labels added at random (c) wrong labels (d) copies of one photo
27. 27.True or false: “new data for testing” is good for training a model. (a) False (b) True
28. 28.Which of these is good for training a model? (a) testing on training data (b) wrong labels (c) clear, sharp examples (d) blurry, unclear photos
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
30. 30.True or false: “many varied examples” is bad for training a model. (a) True (b) False
31. 31.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
32. 32.True or false: “very few examples” is bad for training a model. (a) True (b) False
33. 33.Which of these is bad for training a model? (a) wrong labels (b) clear, sharp examples (c) new data for testing (d) many varied examples
34. 34.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) clear, sharp examples (d) wrong labels
36. 36.True or false: “correct labels” is bad for training a model. (a) False (b) True
37. 37.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
38. 38.True or false: “photos in many lights” is good for training a model. (a) False (b) True
39. 39.True or false: “checking labels twice” is good for training a model. (a) True (b) False
40. 40.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False

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