Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 458 · 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.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) correct labels (d) testing on training data
2. 2.True or false: “correct labels” is bad for training a model. (a) False (b) True
3. 3.🏷️ 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
4. 4.True or false: “labels added at random” is good for training a model. (a) False (b) True
5. 5.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) a sticker on a laptop (d) the right answer tag
6. 6.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) very few examples (d) photos in many lights
7. 7.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
8. 8.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
9. 9.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) photos in many lights (d) testing on training data
10. 10.Which of these is bad for training a model? (a) photos in many lights (b) copies of one photo (c) many varied examples (d) clear, sharp examples
11. 11.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
12. 12.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) copies of one photo (d) blurry, unclear photos
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
15. 15.Which of these is good for training a model? (a) new data for testing (b) only one kind of example (c) wrong labels (d) copies of one photo
16. 16.True or false: “very few examples” is bad for training a model. (a) True (b) False
17. 17.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
18. 18.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) many labelled photos (c) one single photo (d) a song about dogs
19. 19.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
20. 20.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
21. 21.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
22. 22.🏷️ 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
23. 23.True or false: “many varied examples” is good for training a model. (a) True (b) False
24. 24.Which of these is good for training a model? (a) examples of every type (b) testing on training data (c) wrong labels (d) very few examples
25. 25.🔍 Which is the odd one out? (a) examples of every type (b) photos in many lights (c) new data for testing (d) wrong labels
26. 26.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
27. 27.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
28. 28.🏷️ 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
29. 29.Which of these is bad for training a model? (a) clear, sharp examples (b) examples of every type (c) blurry, unclear photos (d) new data for testing
30. 30.🥭 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
31. 31.Which of these is good for training a model? (a) many varied examples (b) testing on training data (c) very few examples (d) wrong labels
32. 32.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
33. 33.🔍 Which is the odd one out? (a) clear, sharp examples (b) very few examples (c) many varied examples (d) photos in many lights
34. 34.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
35. 35.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) photos in many lights (d) testing on training data
36. 36.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) clear, sharp examples (d) labels added at random
37. 37.True or false: “copies of one photo” is good for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
39. 39.Which of these is bad for training a model? (a) examples of every type (b) wrong labels (c) correct labels (d) clear, sharp examples
40. 40.🏷️ 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

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