Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 487 · 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.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the price (d) the font size
2. 2.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
3. 3.Which of these is good for training a model? (a) testing on training data (b) many varied examples (c) very few examples (d) only one kind of example
4. 4.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) better at its task (d) change colour
5. 5.True or false: “very few examples” is bad for training a model. (a) False (b) True
6. 6.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
7. 7.Which of these is bad for training a model? (a) labels added at random (b) photos in many lights (c) new data for testing (d) checking labels twice
8. 8.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) clear, sharp examples (d) copies of one photo
11. 11.🥭 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
12. 12.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) correct labels (d) labels added at random
13. 13.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) very few examples (d) checking labels twice
14. 14.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) examples of every type (d) wrong labels
15. 15.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
16. 16.True or false: “wrong labels” is good for training a model. (a) False (b) True
17. 17.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
18. 18.Which of these is bad for training a model? (a) correct labels (b) very few examples (c) photos in many lights (d) new data for testing
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
20. 20.🔍 Which is the odd one out? (a) checking labels twice (b) clear, sharp examples (c) copies of one photo (d) many varied examples
21. 21.True or false: “many varied examples” is good for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) labels added at random (d) many varied examples
23. 23.True or false: “correct labels” is good for training a model. (a) True (b) False
24. 24.True or false: “examples of every type” is good for training a model. (a) True (b) False
25. 25.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
26. 26.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
27. 27.Which of these is good for training a model? (a) correct labels (b) wrong labels (c) only one kind of example (d) blurry, unclear photos
28. 28.Which of these is good for training a model? (a) labels added at random (b) only one kind of example (c) new data for testing (d) very few examples
29. 29.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) clear, sharp examples (d) very few examples
30. 30.🏷️ 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) spam (b) not spam
31. 31.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
32. 32.🔍 Which is the odd one out? (a) new data for testing (b) only one kind of example (c) clear, sharp examples (d) checking labels twice
33. 33.True or false: “new data for testing” is bad for training a model. (a) False (b) True
34. 34.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
35. 35.🧠 Machine learning is a way for computers to… (a) learn from data (b) clean screens (c) charge faster (d) print pages
36. 36.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) only one kind of example (d) photos in many lights
37. 37.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
38. 38.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
39. 39.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam

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