Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 559 · 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 is the odd one out? (a) labels added at random (b) checking labels twice (c) correct labels (d) new data for testing
2. 2.🏷️ 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
3. 3.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
4. 4.🏷️ 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
5. 5.🏷️ 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
6. 6.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
7. 7.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) new data for testing (d) correct labels
8. 8.Which of these is bad for training a model? (a) very few examples (b) new data for testing (c) clear, sharp examples (d) many varied examples
9. 9.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
10. 10.🏷️ 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
11. 11.🔍 Which is the odd one out? (a) only one kind of example (b) copies of one photo (c) examples of every type (d) wrong labels
12. 12.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) new data for testing (d) testing on training data
13. 13.Which of these is good for training a model? (a) wrong labels (b) only one kind of example (c) blurry, unclear photos (d) correct labels
14. 14.True or false: “checking labels twice” is bad for training a model. (a) False (b) True
15. 15.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) checking labels twice (d) labels added at random
16. 16.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
17. 17.🏷️ 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
18. 18.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
19. 19.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) correct labels (d) blurry, unclear photos
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 bad for training a model? (a) checking labels twice (b) new data for testing (c) wrong labels (d) examples of every type
22. 22.True or false: “wrong labels” is good for training a model. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) new data for testing (d) only one kind of example
24. 24.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
25. 25.🏷️ 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
26. 26.Which of these is good for training a model? (a) only one kind of example (b) clear, sharp examples (c) copies of one photo (d) very few examples
27. 27.Which of these is bad for training a model? (a) labels added at random (b) clear, sharp examples (c) many varied examples (d) correct labels
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) only one kind of example (c) photos in many lights (d) testing on training data
29. 29.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the font size (c) the price (d) a sticker on a laptop
30. 30.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) True (b) False
31. 31.🥭 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
32. 32.Which of these is bad for training a model? (a) correct labels (b) examples of every type (c) new data for testing (d) testing on training data
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) correct labels (b) very few examples (c) testing on training data (d) labels added at random
35. 35.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
36. 36.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
37. 37.🐶 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
38. 38.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
39. 39.True or false: “many varied examples” is bad for training a model. (a) False (b) True
40. 40.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False

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