Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 498 · 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) bad for training (b) good for training
2. 2.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
3. 3.🏷️ 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
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
5. 5.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
6. 6.🏷️ 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
7. 7.True or false: “examples of every type” is bad for training a model. (a) False (b) True
8. 8.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
9. 9.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) only one kind of example (d) examples of every type
10. 10.True or false: “very few examples” is bad for training a model. (a) True (b) False
11. 11.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
12. 12.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
13. 13.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) checking labels twice (d) copies of one photo
14. 14.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
15. 15.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it may get it wrong (d) it will be perfect
16. 16.🏷️ 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
17. 17.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) testing on training data (d) only one kind of example
19. 19.Which of these is good for training a model? (a) clear, sharp examples (b) copies of one photo (c) blurry, unclear photos (d) very few examples
20. 20.🔍 Which is the odd one out? (a) examples of every type (b) very few examples (c) many varied examples (d) new data for testing
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
22. 22.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
24. 24.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) examples of every type (d) wrong labels
26. 26.True or false: “many varied examples” is bad for training a model. (a) True (b) False
27. 27.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) many varied examples (d) wrong labels
28. 28.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
29. 29.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) copies of one photo (d) checking labels twice
30. 30.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) copies of one photo (d) clear, sharp examples
31. 31.Which of these is good for training a model? (a) photos in many lights (b) only one kind of example (c) copies of one photo (d) very few examples
32. 32.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
33. 33.True or false: “many varied examples” is good for training a model. (a) False (b) True
34. 34.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) wrong labels (d) new data for testing
35. 35.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
36. 36.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
37. 37.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
38. 38.True or false: “new data for testing” is good for training a model. (a) False (b) True
39. 39.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
40. 40.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on

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