Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 563 · 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.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
2. 2.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) labels added at random (d) photos in many lights
3. 3.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
4. 4.True or false: “labels added at random” is good for training a model. (a) True (b) False
5. 5.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
6. 6.🏷️ 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
7. 7.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
8. 8.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) correct labels (d) only one kind of example
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
10. 10.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the right answer tag (d) the price
11. 11.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) new data for testing (d) blurry, unclear photos
12. 12.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
13. 13.Which of these is good for training a model? (a) copies of one photo (b) testing on training data (c) examples of every type (d) labels added at random
14. 14.True or false: “very few examples” is good for training a model. (a) False (b) True
15. 15.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
16. 16.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
17. 17.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it may get it wrong (d) it will turn yellow
18. 18.🏷️ 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
19. 19.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
20. 20.🏷️ 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
21. 21.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
22. 22.🏷️ 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
23. 23.Which of these is good for training a model? (a) wrong labels (b) checking labels twice (c) testing on training data (d) copies of one photo
24. 24.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
25. 25.Which of these is good for training a model? (a) copies of one photo (b) correct labels (c) blurry, unclear photos (d) labels added at random
26. 26.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) copies of one photo (d) checking labels twice
27. 27.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) clear, sharp examples (d) only one kind of example
28. 28.True or false: “only one kind of example” is good for training a model. (a) False (b) True
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) very few examples (d) many varied examples
30. 30.True or false: “very few examples” is bad for training a model. (a) True (b) False
31. 31.True or false: “correct labels” is good for training a model. (a) True (b) False
32. 32.True or false: “testing on training data” is bad for training a model. (a) False (b) True
33. 33.True or false: “copies of one photo” is good for training a model. (a) True (b) False
34. 34.True or false: “correct labels” is bad for training a model. (a) False (b) True
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) very few examples (d) new data for testing
36. 36.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
37. 37.🔍 Which is the odd one out? (a) blurry, unclear photos (b) correct labels (c) wrong labels (d) copies of one photo
38. 38.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
39. 39.🏷️ 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) spam (b) not spam
40. 40.True or false: “examples of every type” is bad for training a model. (a) True (b) False

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