Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 503 · 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.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
2. 2.Which of these is bad for training a model? (a) photos in many lights (b) wrong labels (c) clear, sharp examples (d) checking labels twice
3. 3.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it may get it wrong (d) it will shut down
4. 4.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
5. 5.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
6. 6.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
7. 7.🐶 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
8. 8.True or false: “correct labels” is good for training a model. (a) True (b) False
9. 9.Which of these is bad for training a model? (a) many varied examples (b) copies of one photo (c) photos in many lights (d) correct labels
10. 10.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) checking labels twice (d) labels added at random
11. 11.🏷️ 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
12. 12.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) testing on training data (d) new data for testing
13. 13.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) checking labels twice (d) wrong labels
14. 14.Which of these is good for training a model? (a) photos in many lights (b) very few examples (c) testing on training data (d) only one kind of example
15. 15.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) very few examples (d) labels added at random
16. 16.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
17. 17.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
18. 18.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
19. 19.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
20. 20.Which of these is good for training a model? (a) labels added at random (b) many varied examples (c) copies of one photo (d) only one kind of example
21. 21.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
22. 22.🔍 Which is the odd one out? (a) checking labels twice (b) photos in many lights (c) new data for testing (d) testing on training data
23. 23.🧠 Machine learning is a way for computers to… (a) clean screens (b) learn from data (c) print pages (d) charge faster
24. 24.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
25. 25.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) testing on training data (d) new data for testing
26. 26.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
27. 27.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
28. 28.🔍 Which is the odd one out? (a) photos in many lights (b) many varied examples (c) wrong labels (d) new data for testing
29. 29.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
30. 30.🏷️ 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
31. 31.🏷️ 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
32. 32.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) copies of one photo (d) examples of every type
33. 33.🔍 Which is the odd one out? (a) wrong labels (b) examples of every type (c) labels added at random (d) copies of one photo
34. 34.True or false: “photos in many lights” is good for training a model. (a) False (b) True
35. 35.True or false: “new data for testing” is good for training a model. (a) True (b) False
36. 36.True or false: “many varied examples” is bad for training a model. (a) True (b) False
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
39. 39.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
40. 40.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True

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