Machine Learning for Kids Worksheet

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

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