Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 183 · 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: blurry, unclear photos? (a) good for training (b) bad for training
2. 2.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
3. 3.True or false: “wrong labels” is bad for training a model. (a) False (b) True
4. 4.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
5. 5.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
6. 6.🏷️ 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
7. 7.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will turn yellow (b) it will be perfect (c) it will shut down (d) it may get it wrong
8. 8.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
9. 9.🔍 Which is the odd one out? (a) correct labels (b) many varied examples (c) checking labels twice (d) labels added at random
10. 10.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) spam (b) not spam
11. 11.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
12. 12.🔍 Which is the odd one out? (a) blurry, unclear photos (b) examples of every type (c) copies of one photo (d) wrong labels
13. 13.True or false: “many varied examples” is good for training a model. (a) False (b) True
14. 14.True or false: “photos in many lights” is bad for training a model. (a) True (b) False
15. 15.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
16. 16.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
17. 17.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
18. 18.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
19. 19.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
20. 20.🏷️ 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
21. 21.Which of these is good for training a model? (a) very few examples (b) only one kind of example (c) correct labels (d) blurry, unclear photos
22. 22.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
23. 23.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
24. 24.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) correct labels (d) very few examples
25. 25.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
26. 26.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
27. 27.🔍 Which is the odd one out? (a) very few examples (b) labels added at random (c) new data for testing (d) only one kind of example
28. 28.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
29. 29.Which of these is bad for training a model? (a) only one kind of example (b) clear, sharp examples (c) correct labels (d) many varied examples
30. 30.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
31. 31.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Earn ₹50,000 a day from home!!!” (a) not spam (b) spam
32. 32.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
33. 33.Which of these is bad for training a model? (a) labels added at random (b) clear, sharp examples (c) correct labels (d) photos in many lights
34. 34.🔍 Which is the odd one out? (a) wrong labels (b) many varied examples (c) very few examples (d) only one kind of example
35. 35.🔍 Which is the odd one out? (a) new data for testing (b) checking labels twice (c) wrong labels (d) correct labels
36. 36.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
37. 37.🔍 Which is the odd one out? (a) checking labels twice (b) very few examples (c) examples of every type (d) clear, sharp examples
38. 38.True or false: “wrong labels” is good for training a model. (a) True (b) False
39. 39.True or false: “clear, sharp examples” is good for training a model. (a) True (b) False
40. 40.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) very few examples (d) examples of every type

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