Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 56 · 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) wrong labels (b) very few examples (c) only one kind of example (d) clear, sharp examples
2. 2.🏷️ 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
3. 3.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) examples of every type (d) wrong labels
4. 4.🏷️ 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
5. 5.True or false: “correct labels” is bad for training a model. (a) True (b) False
6. 6.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
7. 7.True or false: “blurry, unclear photos” is bad for training a model. (a) False (b) True
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) spam (b) not spam
9. 9.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) a list of rules only (c) many labelled photos (d) one single photo
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
12. 12.🥭 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 be perfect (d) it will shut down
13. 13.True or false: “new data for testing” is good for training a model. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) examples of every type (b) copies of one photo (c) clear, sharp examples (d) checking labels twice
15. 15.True or false: “examples of every type” is bad for training a model. (a) True (b) False
16. 16.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
17. 17.True or false: “photos in many lights” is good for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) testing on training data (b) only one kind of example (c) photos in many lights (d) labels added at random
19. 19.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
20. 20.True or false: “wrong labels” is good for training a model. (a) False (b) True
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) not spam (b) spam
22. 22.🏷️ 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
23. 23.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) labels added at random (d) new data for testing
24. 24.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) correct labels (d) blurry, unclear photos
25. 25.🏷️ 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
26. 26.Which of these is good for training a model? (a) new data for testing (b) labels added at random (c) testing on training data (d) blurry, unclear photos
27. 27.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
28. 28.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
29. 29.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) correct labels (d) photos in many lights
30. 30.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) testing on training data (d) labels added at random
31. 31.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
32. 32.🔍 Which is the odd one out? (a) many varied examples (b) very few examples (c) wrong labels (d) blurry, unclear photos
33. 33.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
34. 34.🔍 Which is the odd one out? (a) clear, sharp examples (b) correct labels (c) examples of every type (d) wrong labels
35. 35.Which of these is good for training a model? (a) blurry, unclear photos (b) copies of one photo (c) wrong labels (d) correct labels
36. 36.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
37. 37.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) blurry, unclear photos (d) examples of every type
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
40. 40.Which of these is good for training a model? (a) examples of every type (b) blurry, unclear photos (c) labels added at random (d) copies of one photo

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