Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 231 · 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.🥭 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 will turn yellow (d) it may get it wrong
2. 2.Which of these is bad for training a model? (a) correct labels (b) clear, sharp examples (c) many varied examples (d) wrong labels
3. 3.True or false: “testing on training data” is good for training a model. (a) False (b) True
4. 4.🔍 Which is the odd one out? (a) copies of one photo (b) wrong labels (c) correct labels (d) blurry, unclear photos
5. 5.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
6. 6.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) slower to switch on (d) forget everything
7. 7.🐶 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
8. 8.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) False (b) True
9. 9.True or false: “labels added at random” is bad for training a model. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) clear, sharp examples (d) very few examples
11. 11.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) many varied examples (d) copies of one photo
12. 12.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) photos in many lights (d) many varied examples
13. 13.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
14. 14.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
15. 15.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
16. 16.True or false: “examples of every type” is good for training a model. (a) False (b) True
17. 17.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
18. 18.Which of these is good for training a model? (a) examples of every type (b) only one kind of example (c) very few examples (d) wrong labels
19. 19.🏷️ 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
20. 20.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
21. 21.True or false: “wrong labels” is good for training a model. (a) False (b) True
22. 22.🏷️ 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
23. 23.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
24. 24.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
25. 25.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
26. 26.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
27. 27.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
28. 28.True or false: “copies of one photo” is good for training a model. (a) False (b) True
29. 29.🏷️ 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
30. 30.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
31. 31.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
32. 32.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
33. 33.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
34. 34.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
35. 35.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
36. 36.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) copies of one photo (d) new data for testing
37. 37.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the right answer tag (d) the font size
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
39. 39.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
40. 40.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) False (b) True

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