Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 200 · 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) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
2. 2.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
3. 3.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) examples of every type (d) only one kind of example
4. 4.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
5. 5.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
6. 6.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) testing on training data (d) clear, sharp examples
8. 8.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
9. 9.🔍 Which is the odd one out? (a) wrong labels (b) copies of one photo (c) very few examples (d) photos in many lights
10. 10.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) labels added at random (d) checking labels twice
11. 11.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) very few examples (d) correct labels
12. 12.Which of these is good for training a model? (a) many varied examples (b) blurry, unclear photos (c) only one kind of example (d) labels added at random
13. 13.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
14. 14.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
15. 15.🏷️ 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
16. 16.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
17. 17.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
18. 18.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
19. 19.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) blurry, unclear photos (d) new data for testing
20. 20.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
21. 21.Which of these is good for training a model? (a) blurry, unclear photos (b) only one kind of example (c) new data for testing (d) very few examples
22. 22.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
23. 23.🏷️ 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
24. 24.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) checking labels twice (d) wrong labels
25. 25.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
26. 26.True or false: “photos in many lights” is good for training a model. (a) False (b) True
27. 27.Which of these is bad for training a model? (a) checking labels twice (b) clear, sharp examples (c) photos in many lights (d) only one kind of example
28. 28.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) examples of every type (d) very few examples
29. 29.🏷️ 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
30. 30.True or false: “many varied examples” is bad for training a model. (a) False (b) True
31. 31.True or false: “many varied examples” is good for training a model. (a) True (b) False
32. 32.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) only one kind of example (d) correct labels
33. 33.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: “very few examples” is bad for training a model. (a) False (b) True
35. 35.🔍 Which is the odd one out? (a) testing on training data (b) copies of one photo (c) labels added at random (d) examples of every type
36. 36.🏷️ 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
37. 37.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
38. 38.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
39. 39.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) copies of one photo (d) checking labels twice

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