Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 467 · 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 is the odd one out? (a) very few examples (b) testing on training data (c) blurry, unclear photos (d) photos in many lights
2. 2.Which of these is good for training a model? (a) very few examples (b) labels added at random (c) copies of one photo (d) correct labels
3. 3.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
4. 4.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) spam (b) not spam
5. 5.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) examples of every type (d) wrong labels
7. 7.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
8. 8.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will be perfect (d) it will turn yellow
9. 9.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) many varied examples (d) labels added at random
10. 10.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
11. 11.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
12. 12.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
13. 13.True or false: “many varied examples” is bad for training a model. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) blurry, unclear photos (b) many varied examples (c) examples of every type (d) checking labels twice
15. 15.True or false: “wrong labels” is good for training a model. (a) False (b) True
16. 16.Which of these is good for training a model? (a) copies of one photo (b) blurry, unclear photos (c) testing on training data (d) checking labels twice
17. 17.🔍 Which is the odd one out? (a) copies of one photo (b) very few examples (c) checking labels twice (d) only one kind of example
18. 18.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
19. 19.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
20. 20.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
21. 21.🏷️ 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
22. 22.🐶 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
23. 23.🔍 Which is the odd one out? (a) correct labels (b) very few examples (c) many varied examples (d) new data for testing
24. 24.🏷️ 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
25. 25.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
26. 26.Which of these is good for training a model? (a) testing on training data (b) clear, sharp examples (c) very few examples (d) blurry, unclear photos
27. 27.Which of these is bad for training a model? (a) many varied examples (b) labels added at random (c) clear, sharp examples (d) photos in many lights
28. 28.🏷️ 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
29. 29.🔍 Which is the odd one out? (a) correct labels (b) clear, sharp examples (c) examples of every type (d) copies of one photo
30. 30.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) labels added at random (d) clear, sharp examples
31. 31.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
32. 32.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) correct labels (d) new data for testing
33. 33.Which of these is bad for training a model? (a) copies of one photo (b) many varied examples (c) new data for testing (d) examples of every type
34. 34.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
35. 35.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) testing on training data (d) clear, sharp examples
36. 36.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
37. 37.🏷️ 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
38. 38.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) blurry, unclear photos (d) testing on training data
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) new data for testing (c) testing on training data (d) wrong labels
40. 40.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True

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