Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 123 · 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.🏷️ 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
2. 2.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
3. 3.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
4. 4.🔍 Which is the odd one out? (a) examples of every type (b) copies of one photo (c) blurry, unclear photos (d) very few examples
5. 5.True or false: “new data for testing” is good for training a model. (a) True (b) False
6. 6.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
7. 7.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
8. 8.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) photos in many lights (d) testing on training data
9. 9.🐶 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
10. 10.Which of these is good for training a model? (a) clear, sharp examples (b) labels added at random (c) blurry, unclear photos (d) wrong labels
11. 11.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) clear, sharp examples (d) testing on training data
12. 12.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
13. 13.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
14. 14.🏷️ 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
15. 15.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
16. 16.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
17. 17.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) photos in many lights (d) copies of one photo
18. 18.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
19. 19.🔍 Which is the odd one out? (a) only one kind of example (b) correct labels (c) very few examples (d) copies of one photo
20. 20.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) many varied examples (d) labels added at random
21. 21.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) correct labels (d) very few examples
22. 22.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
23. 23.Which of these is bad for training a model? (a) photos in many lights (b) blurry, unclear photos (c) checking labels twice (d) new data for testing
24. 24.📚 Usually, more good examples make a model… (a) forget everything (b) slower to switch on (c) change colour (d) better at its task
25. 25.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
26. 26.True or false: “many varied examples” is good for training a model. (a) False (b) True
27. 27.🏷️ 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
28. 28.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
29. 29.True or false: “examples of every type” is bad for training a model. (a) True (b) False
30. 30.True or false: “testing on training data” is bad for training a model. (a) False (b) True
31. 31.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
32. 32.🏷️ 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) not spam (b) spam
33. 33.🥭 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
34. 34.🏷️ 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
35. 35.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
36. 36.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
37. 37.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) very few examples (b) examples of every type (c) photos in many lights (d) new data for testing
39. 39.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) a sticker on a laptop (d) the price
40. 40.Which of these is bad for training a model? (a) many varied examples (b) copies of one photo (c) new data for testing (d) clear, sharp examples

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