Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 145 · 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.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) charge faster (d) learn from data
2. 2.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) checking labels twice (d) new data for testing
3. 3.Which of these is bad for training a model? (a) new data for testing (b) testing on training data (c) clear, sharp examples (d) many varied examples
4. 4.🏷️ What is a “label” in machine learning? (a) the font size (b) a sticker on a laptop (c) the price (d) the right answer tag
5. 5.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
6. 6.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
7. 7.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) testing on training data (d) copies of one photo
8. 8.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
9. 9.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
10. 10.True or false: “correct labels” is good for training a model. (a) True (b) False
11. 11.🥭 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
12. 12.🔍 Which is the odd one out? (a) clear, sharp examples (b) only one kind of example (c) blurry, unclear photos (d) very few examples
13. 13.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
14. 14.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) False (b) True
15. 15.Which of these is good for training a model? (a) checking labels twice (b) blurry, unclear photos (c) very few examples (d) wrong labels
16. 16.True or false: “many varied examples” is bad for training a model. (a) True (b) False
17. 17.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) labels added at random (d) examples of every type
18. 18.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
19. 19.Which of these is bad for training a model? (a) photos in many lights (b) examples of every type (c) very few examples (d) many varied examples
20. 20.📚 Good or bad for training a model: labels added at random? (a) good for training (b) bad for training
21. 21.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
22. 22.🏷️ 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
23. 23.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “spam”. (a) False (b) True
24. 24.True or false: “examples of every type” is bad for training a model. (a) True (b) False
25. 25.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
26. 26.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
27. 27.True or false: “testing on training data” is bad for training a model. (a) True (b) False
28. 28.🏷️ 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
29. 29.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
30. 30.True or false: “new data for testing” is bad for training a model. (a) True (b) False
31. 31.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
32. 32.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
33. 33.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
34. 34.True or false: “examples of every type” is good for training a model. (a) False (b) True
35. 35.Which of these is good for training a model? (a) testing on training data (b) new data for testing (c) labels added at random (d) blurry, unclear photos
36. 36.🔍 Which is the odd one out? (a) blurry, unclear photos (b) copies of one photo (c) labels added at random (d) correct labels
37. 37.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
39. 39.True or false: “labels added at random” is bad for training a model. (a) False (b) True
40. 40.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training

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