Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 228 · 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: “wrong labels” is bad for training a model. (a) False (b) True
2. 2.True or false: “very few examples” is bad for training a model. (a) True (b) False
3. 3.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
4. 4.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) wrong labels (d) examples of every type
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
6. 6.Which of these is bad for training a model? (a) examples of every type (b) clear, sharp examples (c) labels added at random (d) checking labels twice
7. 7.Which of these is good for training a model? (a) new data for testing (b) blurry, unclear photos (c) very few examples (d) wrong labels
8. 8.Which of these is bad for training a model? (a) checking labels twice (b) many varied examples (c) correct labels (d) testing on training data
9. 9.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
11. 11.🔍 Which is the odd one out? (a) correct labels (b) only one kind of example (c) labels added at random (d) testing on training data
12. 12.Which of these is good for training a model? (a) labels added at random (b) correct labels (c) blurry, unclear photos (d) testing on training data
13. 13.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
15. 15.True or false: “many varied examples” is bad for training a model. (a) False (b) True
16. 16.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
17. 17.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
18. 18.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
19. 19.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) blurry, unclear photos (d) correct labels
20. 20.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) copies of one photo (d) checking labels twice
21. 21.🏷️ 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
22. 22.True or false: “correct labels” is bad for training a model. (a) False (b) True
23. 23.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will shut down (d) it will be perfect
24. 24.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) new data for testing (d) copies of one photo
25. 25.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True
26. 26.True or false: “very few examples” is good for training a model. (a) True (b) False
27. 27.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
28. 28.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
29. 29.True or false: “copies of one photo” is good for training a model. (a) True (b) False
30. 30.🏷️ 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
31. 31.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
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) spam (b) not spam
33. 33.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
34. 34.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) wrong labels (d) examples of every type
35. 35.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
36. 36.🔍 Which is the odd one out? (a) copies of one photo (b) testing on training data (c) clear, sharp examples (d) blurry, unclear photos
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) labels added at random (d) checking labels twice
38. 38.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a list of rules only (d) a song about dogs
39. 39.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
40. 40.True or false: “examples of every type” is good for training a model. (a) False (b) True

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