Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 470 · 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 “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
2. 2.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
3. 3.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
4. 4.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) testing on training data (d) examples of every type
5. 5.Which of these is bad for training a model? (a) examples of every type (b) very few examples (c) correct labels (d) new data for testing
6. 6.Which of these is good for training a model? (a) new data for testing (b) very few examples (c) only one kind of example (d) testing on training data
7. 7.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) checking labels twice (d) wrong labels
8. 8.🔍 Which is the odd one out? (a) photos in many lights (b) labels added at random (c) very few examples (d) only one kind of example
9. 9.True or false: “examples of every type” is good for training a model. (a) False (b) True
10. 10.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
11. 11.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it will turn yellow (d) it may get it wrong
12. 12.🔍 Which is the odd one out? (a) new data for testing (b) clear, sharp examples (c) blurry, unclear photos (d) examples of every type
13. 13.🔍 Which is the odd one out? (a) many varied examples (b) labels added at random (c) photos in many lights (d) clear, sharp examples
14. 14.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) correct labels (d) testing on training data
15. 15.Which of these is good for training a model? (a) very few examples (b) photos in many lights (c) blurry, unclear photos (d) only one kind of example
16. 16.🏷️ 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
17. 17.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) a sticker on a laptop (d) the price
18. 18.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
19. 19.🏷️ 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
20. 20.🏷️ 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
21. 21.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) checking labels twice (d) testing on training data
22. 22.Which of these is bad for training a model? (a) labels added at random (b) new data for testing (c) photos in many lights (d) many varied examples
23. 23.True or false: “labels added at random” is good for training a model. (a) True (b) False
24. 24.True or false: “wrong labels” is bad for training a model. (a) False (b) True
25. 25.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) blurry, unclear photos (d) many varied examples
26. 26.True or false: “new data for testing” is good for training a model. (a) True (b) False
27. 27.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) labels added at random (d) many varied examples
28. 28.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
30. 30.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) clear, sharp examples (d) photos in many lights
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
32. 32.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) checking labels twice (d) wrong labels
33. 33.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
34. 34.🏷️ 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
35. 35.True or false: “correct labels” is good for training a model. (a) False (b) True
36. 36.📚 Good or bad for training a model: testing on training data? (a) bad for training (b) good for training
37. 37.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
38. 38.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
39. 39.Which of these is good for training a model? (a) correct labels (b) blurry, unclear photos (c) labels added at random (d) only one kind of example
40. 40.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training

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