Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 73 · 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.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
2. 2.Which of these is good for training a model? (a) labels added at random (b) testing on training data (c) copies of one photo (d) checking labels twice
3. 3.🏷️ What is a “label” in machine learning? (a) the price (b) a sticker on a laptop (c) the font size (d) the right answer tag
4. 4.🏷️ 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
5. 5.🔍 Which is the odd one out? (a) testing on training data (b) photos in many lights (c) only one kind of example (d) labels added at random
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 good for training a model? (a) labels added at random (b) blurry, unclear photos (c) testing on training data (d) photos in many lights
8. 8.🏷️ 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
9. 9.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
10. 10.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
11. 11.True or false: “testing on training data” is good for training a model. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) very few examples (b) blurry, unclear photos (c) checking labels twice (d) wrong labels
13. 13.Which of these is bad for training a model? (a) only one kind of example (b) new data for testing (c) correct labels (d) examples of every type
14. 14.🔍 Which is the odd one out? (a) copies of one photo (b) examples of every type (c) testing on training data (d) very few examples
15. 15.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
16. 16.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) testing on training data (d) correct labels
17. 17.Which of these is good for training a model? (a) clear, sharp examples (b) blurry, unclear photos (c) wrong labels (d) copies of one photo
18. 18.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
19. 19.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
20. 20.🧠 Machine learning is a way for computers to… (a) clean screens (b) charge faster (c) print pages (d) learn from data
21. 21.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) clear, sharp examples (d) photos in many lights
22. 22.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
23. 23.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
24. 24.True or false: “testing on training data” is bad for training a model. (a) True (b) False
25. 25.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
26. 26.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
27. 27.🐶 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
28. 28.🥭 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
29. 29.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) spam (b) not spam
31. 31.🔍 Which is the odd one out? (a) only one kind of example (b) clear, sharp examples (c) testing on training data (d) blurry, unclear photos
32. 32.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
33. 33.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
34. 34.Which of these is bad for training a model? (a) checking labels twice (b) photos in many lights (c) testing on training data (d) clear, sharp examples
35. 35.True or false: “correct labels” is good for training a model. (a) False (b) True
36. 36.Which of these is bad for training a model? (a) new data for testing (b) wrong labels (c) checking labels twice (d) examples of every type
37. 37.Which of these is bad for training a model? (a) copies of one photo (b) clear, sharp examples (c) examples of every type (d) checking labels twice
38. 38.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
39. 39.Which of these is good for training a model? (a) blurry, unclear photos (b) testing on training data (c) many varied examples (d) copies of one photo
40. 40.🏷️ 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

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