Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 304 · 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.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
2. 2.🏷️ 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
3. 3.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
4. 4.🔍 Which is the odd one out? (a) checking labels twice (b) blurry, unclear photos (c) photos in many lights (d) clear, sharp examples
5. 5.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) very few examples (d) clear, sharp examples
6. 6.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
7. 7.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
8. 8.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
9. 9.Which of these is bad for training a model? (a) clear, sharp examples (b) testing on training data (c) examples of every type (d) checking labels twice
10. 10.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) many varied examples (d) labels added at random
12. 12.True or false: “correct labels” is bad for training a model. (a) False (b) True
13. 13.🏷️ 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
14. 14.True or false: “photos in many lights” is good for training a model. (a) True (b) False
15. 15.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) photos in many lights (d) only one kind of example
16. 16.🏷️ 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
17. 17.🏷️ 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
18. 18.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
19. 19.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
20. 20.🏷️ 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
21. 21.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) wrong labels (d) correct labels
22. 22.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
23. 23.Which of these is good for training a model? (a) labels added at random (b) new data for testing (c) blurry, unclear photos (d) only one kind of example
24. 24.Which of these is bad for training a model? (a) examples of every type (b) new data for testing (c) only one kind of example (d) correct labels
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
26. 26.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
27. 27.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
28. 28.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) clear, sharp examples (d) wrong labels
29. 29.True or false: “examples of every type” is bad for training a model. (a) True (b) False
30. 30.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) labels added at random (d) clear, sharp examples
31. 31.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
32. 32.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
33. 33.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) very few examples (d) blurry, unclear photos
34. 34.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
35. 35.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) copies of one photo (d) examples of every type
36. 36.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
37. 37.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
38. 38.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
39. 39.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “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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