Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 340 · 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.Which of these is bad for training a model? (a) blurry, unclear photos (b) new data for testing (c) examples of every type (d) checking labels twice
2. 2.Which of these is good for training a model? (a) many varied examples (b) copies of one photo (c) wrong labels (d) testing on training data
3. 3.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) checking labels twice (d) only one kind of example
4. 4.True or false: “only one kind of example” is good for training a model. (a) False (b) True
5. 5.Which of these is good for training a model? (a) new data for testing (b) copies of one photo (c) only one kind of example (d) testing on training data
6. 6.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
7. 7.🥭 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 shut down (d) it will be perfect
8. 8.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
9. 9.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
10. 10.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) better at its task (d) forget everything
11. 11.🏷️ 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
12. 12.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) many varied examples (d) blurry, unclear photos
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.🏷️ 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
15. 15.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
16. 16.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
17. 17.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
18. 18.🏷️ 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
19. 19.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
20. 20.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
21. 21.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
22. 22.🧠 Machine learning is a way for computers to… (a) charge faster (b) learn from data (c) clean screens (d) print pages
23. 23.Which of these is bad for training a model? (a) checking labels twice (b) correct labels (c) photos in many lights (d) only one kind of example
24. 24.Which of these is good for training a model? (a) clear, sharp examples (b) only one kind of example (c) blurry, unclear photos (d) wrong labels
25. 25.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) wrong labels (d) checking labels twice
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
27. 27.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
28. 28.True or false: “very few examples” is good for training a model. (a) False (b) True
29. 29.🐶 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
30. 30.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) clear, sharp examples (d) many varied examples
31. 31.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
32. 32.Which of these is good for training a model? (a) copies of one photo (b) very few examples (c) wrong labels (d) correct labels
33. 33.Which of these is bad for training a model? (a) photos in many lights (b) new data for testing (c) checking labels twice (d) wrong labels
34. 34.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
35. 35.True or false: “correct labels” is bad for training a model. (a) True (b) False
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.True or false: “many varied examples” is bad for training a model. (a) False (b) True
38. 38.Which of these is bad for training a model? (a) copies of one photo (b) checking labels twice (c) correct labels (d) photos in many lights
39. 39.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
40. 40.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training

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