Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 484 · 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.🏷️ 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
2. 2.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) only one kind of example (d) photos in many lights
3. 3.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
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: “examples of every type” is bad for training a model. (a) False (b) True
6. 6.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
7. 7.True or false: “testing on training data” is bad for training a model. (a) True (b) False
8. 8.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
9. 9.True or false: “very few examples” is good for training a model. (a) True (b) False
10. 10.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
11. 11.True or false: “examples of every type” is good for training a model. (a) True (b) False
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) many varied examples (c) examples of every type (d) checking labels twice
13. 13.Which of these is good for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) very few examples (d) copies of one photo
14. 14.Which of these is bad for training a model? (a) correct labels (b) wrong labels (c) examples of every type (d) many varied examples
15. 15.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
16. 16.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
17. 17.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
18. 18.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will be perfect (c) it will turn yellow (d) it may get it wrong
19. 19.True or false: “photos in many lights” is good for training a model. (a) False (b) True
20. 20.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) photos in many lights (d) very few examples
21. 21.🏷️ 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
22. 22.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
23. 23.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
24. 24.Which of these is bad for training a model? (a) clear, sharp examples (b) new data for testing (c) very few examples (d) photos in many lights
25. 25.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
26. 26.True or false: “many varied examples” is bad for training a model. (a) False (b) True
27. 27.Which of these is good for training a model? (a) very few examples (b) examples of every type (c) testing on training data (d) copies of one photo
28. 28.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) photos in many lights (d) examples of every type
29. 29.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam
31. 31.True or false: “wrong labels” is good for training a model. (a) True (b) False
32. 32.True or false: “wrong labels” is bad for training a model. (a) True (b) False
33. 33.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
34. 34.Which of these is good for training a model? (a) correct labels (b) blurry, unclear photos (c) wrong labels (d) very few examples
35. 35.True or false: “clear, sharp examples” is bad for training a model. (a) False (b) True
36. 36.True or false: “very few examples” is bad for training a model. (a) True (b) False
37. 37.True or false: “blurry, unclear photos” is bad for training a model. (a) True (b) False
38. 38.🏷️ 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
39. 39.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) copies of one photo (d) blurry, unclear photos
40. 40.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training

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