Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 138 · 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: “very few examples” is bad for training a model. (a) True (b) False
2. 2.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) correct labels (d) copies of one photo
3. 3.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) forget everything (d) better at its task
4. 4.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
5. 5.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
6. 6.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) correct labels (d) checking labels twice
7. 7.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
8. 8.Which of these is good for training a model? (a) many varied examples (b) copies of one photo (c) testing on training data (d) blurry, unclear photos
9. 9.🏷️ 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
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
11. 11.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
12. 12.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
13. 13.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
14. 14.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
15. 15.True or false: “labels added at random” is bad for training a model. (a) True (b) False
16. 16.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
17. 17.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
18. 18.🏷️ 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
19. 19.🏷️ 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
20. 20.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
21. 21.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) many varied examples (d) checking labels twice
22. 22.🔍 Which is the odd one out? (a) very few examples (b) testing on training data (c) wrong labels (d) many varied examples
23. 23.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
24. 24.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
25. 25.🔍 Which is the odd one out? (a) testing on training data (b) clear, sharp examples (c) many varied examples (d) examples of every type
26. 26.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) testing on training data (d) examples of every type
27. 27.Which of these is good for training a model? (a) very few examples (b) wrong labels (c) blurry, unclear photos (d) new data for testing
28. 28.Which of these is good for training a model? (a) photos in many lights (b) very few examples (c) copies of one photo (d) labels added at random
29. 29.Which of these is bad for training a model? (a) new data for testing (b) very few examples (c) checking labels twice (d) examples of every type
30. 30.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
31. 31.🔍 Which is the odd one out? (a) clear, sharp examples (b) correct labels (c) many varied examples (d) wrong labels
32. 32.🏷️ 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
33. 33.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
34. 34.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
35. 35.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) many labelled photos (d) a list of rules only
36. 36.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) very few examples (d) new data for testing
37. 37.🔍 Which is the odd one out? (a) new data for testing (b) copies of one photo (c) correct labels (d) checking labels twice
38. 38.🥭 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 be perfect (d) it will shut down
39. 39.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
40. 40.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False

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