Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 52 · 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.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “The class picnic is on Friday at 8 am” (a) not spam (b) spam
3. 3.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
5. 5.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
6. 6.True or false: “only one kind of example” is bad for training a model. (a) True (b) False
7. 7.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
8. 8.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
9. 9.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
10. 10.🏷️ 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
11. 11.True or false: “photos in many lights” is good for training a model. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) correct labels (d) photos in many lights
13. 13.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) many varied examples (d) correct labels
14. 14.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) copies of one photo (d) examples of every type
15. 15.Which of these is good for training a model? (a) many varied examples (b) wrong labels (c) blurry, unclear photos (d) copies of one photo
16. 16.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
17. 17.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
18. 18.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
19. 19.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
20. 20.🔍 Which is the odd one out? (a) testing on training data (b) new data for testing (c) examples of every type (d) many varied examples
21. 21.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
22. 22.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
24. 24.🏷️ 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
25. 25.🏷️ 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
26. 26.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) correct labels (d) wrong labels
27. 27.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) copies of one photo (d) correct labels
28. 28.Which of these is bad for training a model? (a) clear, sharp examples (b) correct labels (c) checking labels twice (d) only one kind of example
29. 29.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
30. 30.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) photos in many lights (d) blurry, unclear photos
31. 31.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) examples of every type (d) labels added at random
32. 32.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) very few examples (d) testing on training data
33. 33.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
34. 34.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
35. 35.🔍 Which is the odd one out? (a) checking labels twice (b) wrong labels (c) many varied examples (d) examples of every type
36. 36.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
37. 37.🏷️ 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
38. 38.Which of these is bad for training a model? (a) wrong labels (b) many varied examples (c) photos in many lights (d) correct labels
39. 39.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
40. 40.🏷️ 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

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