Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 141 · 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) new data for testing (b) checking labels twice (c) blurry, unclear photos (d) many varied examples
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) clear, sharp examples (d) testing on training data
3. 3.Which of these is bad for training a model? (a) very few examples (b) many varied examples (c) examples of every type (d) new data for testing
4. 4.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the right answer tag (d) the font size
5. 5.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
6. 6.🏷️ 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
7. 7.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) examples of every type (d) labels added at random
8. 8.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) correct labels (d) copies of one photo
9. 9.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
10. 10.Which of these is bad for training a model? (a) many varied examples (b) new data for testing (c) copies of one photo (d) correct labels
11. 11.🔍 Which is the odd one out? (a) photos in many lights (b) only one kind of example (c) very few examples (d) blurry, unclear photos
12. 12.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it may get it wrong (d) it will be perfect
13. 13.🔍 Which is the odd one out? (a) wrong labels (b) testing on training data (c) blurry, unclear photos (d) new data for testing
14. 14.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) many labelled photos (d) one single photo
15. 15.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
16. 16.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) many varied examples (d) new data for testing
17. 17.Which of these is bad for training a model? (a) new data for testing (b) examples of every type (c) only one kind of example (d) clear, sharp examples
18. 18.🏷️ 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
19. 19.Which of these is good for training a model? (a) copies of one photo (b) correct labels (c) labels added at random (d) wrong labels
20. 20.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
21. 21.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
22. 22.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
23. 23.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
24. 24.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) wrong labels (d) new data for testing
25. 25.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
26. 26.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) True (b) False
27. 27.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
28. 28.🏷️ 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
29. 29.🏷️ 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
30. 30.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
31. 31.True or false: “labels added at random” is good for training a model. (a) False (b) True
32. 32.True or false: “many varied examples” is good for training a model. (a) False (b) True
33. 33.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) very few examples (d) photos in many lights
34. 34.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
35. 35.🏷️ 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
36. 36.Which of these is bad for training a model? (a) many varied examples (b) checking labels twice (c) examples of every type (d) wrong labels
37. 37.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
38. 38.True or false: “wrong labels” is bad for training a model. (a) True (b) False
39. 39.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
40. 40.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False

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