Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 365 · 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.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
2. 2.🏷️ 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
3. 3.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) clear, sharp examples (d) copies of one photo
4. 4.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) testing on training data (d) correct labels
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a song about dogs (c) one single photo (d) a list of rules only
7. 7.True or false: “copies of one photo” is good for training a model. (a) True (b) False
8. 8.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
9. 9.Which of these is bad for training a model? (a) labels added at random (b) new data for testing (c) photos in many lights (d) many varied examples
10. 10.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
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 shut down (c) it may get it wrong (d) it will turn yellow
12. 12.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) only one kind of example (d) clear, sharp examples
13. 13.Which of these is bad for training a model? (a) checking labels twice (b) wrong labels (c) clear, sharp examples (d) correct labels
14. 14.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
15. 15.Which of these is good for training a model? (a) new data for testing (b) very few examples (c) only one kind of example (d) labels added at random
16. 16.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
17. 17.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
18. 18.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
19. 19.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) correct labels (d) labels added at random
20. 20.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
21. 21.🏷️ 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
22. 22.Which of these is good for training a model? (a) photos in many lights (b) only one kind of example (c) blurry, unclear photos (d) very few examples
23. 23.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
24. 24.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) photos in many lights (d) new data for testing
25. 25.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) examples of every type (d) checking labels twice
28. 28.True or false: “new data for testing” is bad for training a model. (a) True (b) False
29. 29.🏷️ 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
30. 30.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
31. 31.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
32. 32.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) many varied examples (d) only one kind of example
33. 33.🏷️ 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
34. 34.🏷️ 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
35. 35.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) copies of one photo (d) examples of every type
36. 36.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) many varied examples (b) clear, sharp examples (c) labels added at random (d) correct labels
38. 38.Which of these is bad for training a model? (a) blurry, unclear photos (b) checking labels twice (c) clear, sharp examples (d) new data for testing
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) examples of every type (d) wrong labels
40. 40.🏷️ 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

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