Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 507 · 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.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
2. 2.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
3. 3.True or false: “examples of every type” is bad for training a model. (a) False (b) True
4. 4.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) photos in many lights (d) copies of one photo
5. 5.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
6. 6.🏷️ 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
7. 7.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
8. 8.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
9. 9.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
10. 10.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) a list of rules only (c) a song about dogs (d) one single photo
11. 11.🔍 Which is the odd one out? (a) many varied examples (b) labels added at random (c) photos in many lights (d) clear, sharp examples
12. 12.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
13. 13.Which of these is bad for training a model? (a) examples of every type (b) only one kind of example (c) checking labels twice (d) new data for testing
14. 14.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
15. 15.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
16. 16.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) photos in many lights (d) labels added at random
17. 17.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
18. 18.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will shut down (c) it will be perfect (d) it may get it wrong
19. 19.🔍 Which is the odd one out? (a) blurry, unclear photos (b) wrong labels (c) examples of every type (d) very few examples
20. 20.🔍 Which is the odd one out? (a) checking labels twice (b) testing on training data (c) copies of one photo (d) very few examples
21. 21.📚 Usually, more good examples make a model… (a) slower to switch on (b) change colour (c) forget everything (d) better at its task
22. 22.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) charge faster (d) clean screens
23. 23.True or false: “correct labels” is bad for training a model. (a) False (b) True
24. 24.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) False (b) True
25. 25.🏷️ 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
26. 26.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) very few examples (d) photos in many lights
27. 27.Which of these is bad for training a model? (a) clear, sharp examples (b) very few examples (c) photos in many lights (d) many varied examples
28. 28.True or false: “examples of every type” is good for training a model. (a) True (b) False
29. 29.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
31. 31.Which of these is good for training a model? (a) very few examples (b) clear, sharp examples (c) copies of one photo (d) testing on training data
32. 32.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
33. 33.True or false: “wrong labels” is good for training a model. (a) True (b) False
34. 34.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) examples of every type (d) testing on training data
35. 35.🏷️ 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
36. 36.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
37. 37.🔍 Which is the odd one out? (a) checking labels twice (b) correct labels (c) blurry, unclear photos (d) new data for testing
38. 38.True or false: “photos in many lights” is good for training a model. (a) False (b) True
39. 39.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
40. 40.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) many varied examples (d) very few examples

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