Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 508 · 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 “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
2. 2.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) photos in many lights (d) very few examples
3. 3.🔍 Which is the odd one out? (a) correct labels (b) new data for testing (c) checking labels twice (d) only one kind of example
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) very few examples (b) checking labels twice (c) wrong labels (d) blurry, unclear photos
6. 6.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
7. 7.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) better at its task (d) slower to switch on
8. 8.Which of these is bad for training a model? (a) blurry, unclear photos (b) new data for testing (c) correct labels (d) photos in many lights
9. 9.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) clear, sharp examples (d) checking labels twice
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.🏷️ 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
12. 12.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) copies of one photo (d) many varied examples
13. 13.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
14. 14.Which of these is good for training a model? (a) many varied examples (b) very few examples (c) wrong labels (d) labels added at random
15. 15.🔍 Which is the odd one out? (a) very few examples (b) only one kind of example (c) copies of one photo (d) correct labels
16. 16.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
17. 17.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
18. 18.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) labels added at random (d) clear, sharp examples
19. 19.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
20. 20.🏷️ 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
21. 21.🏷️ 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
22. 22.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) clear, sharp examples (b) testing on training data (c) photos in many lights (d) many varied examples
24. 24.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
25. 25.Which of these is bad for training a model? (a) new data for testing (b) correct labels (c) wrong labels (d) examples of every type
26. 26.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
27. 27.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
28. 28.True or false: “wrong labels” is bad for training a model. (a) False (b) True
29. 29.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
30. 30.🏷️ 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
31. 31.True or false: “examples of every type” is good for training a model. (a) True (b) False
32. 32.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) checking labels twice (d) copies of one photo
33. 33.🔍 Which is the odd one out? (a) wrong labels (b) labels added at random (c) examples of every type (d) only one kind of example
34. 34.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
35. 35.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
36. 36.🏷️ 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
37. 37.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
38. 38.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) wrong labels (d) correct labels
39. 39.🏷️ 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
40. 40.🥭 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 may get it wrong (d) it will turn yellow

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