Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 203 · 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 song about dogs (c) one single photo (d) a list of rules only
2. 2.True or false: “very few examples” is bad for training a model. (a) False (b) True
3. 3.True or false: “labels added at random” is bad for training a model. (a) True (b) False
4. 4.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) very few examples (d) correct labels
5. 5.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) new data for testing (d) very few examples
6. 6.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) labels added at random (d) copies of one photo
7. 7.True or false: “wrong labels” is good for training a model. (a) False (b) True
8. 8.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
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 turn yellow (b) it may get it wrong (c) it will be perfect (d) it will shut down
11. 11.🔍 Which is the odd one out? (a) clear, sharp examples (b) wrong labels (c) only one kind of example (d) copies of one photo
12. 12.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) very few examples (d) checking labels twice
14. 14.🏷️ 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
15. 15.Which of these is bad for training a model? (a) many varied examples (b) copies of one photo (c) clear, sharp examples (d) photos in many lights
16. 16.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
17. 17.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
18. 18.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
19. 19.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) copies of one photo (d) labels added at random
20. 20.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
21. 21.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
22. 22.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
23. 23.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
24. 24.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
25. 25.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) only one kind of example (d) wrong labels
27. 27.True or false: “testing on training data” is good for training a model. (a) True (b) False
28. 28.🏷️ 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
29. 29.🔍 Which is the odd one out? (a) clear, sharp examples (b) labels added at random (c) examples of every type (d) new data for testing
30. 30.🏷️ 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
31. 31.🔍 Which is the odd one out? (a) examples of every type (b) wrong labels (c) new data for testing (d) many varied examples
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
33. 33.True or false: “new data for testing” is bad for training a model. (a) True (b) False
34. 34.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
35. 35.True or false: “wrong labels” is bad for training a model. (a) True (b) False
36. 36.Which of these is bad for training a model? (a) examples of every type (b) testing on training data (c) photos in many lights (d) checking labels twice
37. 37.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) testing on training data (d) clear, sharp examples
38. 38.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
39. 39.Which of these is good for training a model? (a) new data for testing (b) wrong labels (c) labels added at random (d) copies of one photo
40. 40.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training

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