Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 80 · 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 of these is bad for training a model? (a) many varied examples (b) clear, sharp examples (c) checking labels twice (d) very few examples
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
3. 3.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) many labelled photos (d) a song about dogs
4. 4.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) wrong labels (d) new data for testing
5. 5.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
6. 6.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) many varied examples (d) photos in many lights
7. 7.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the font size (c) the right answer tag (d) the price
8. 8.Which of these is good for training a model? (a) copies of one photo (b) labels added at random (c) wrong labels (d) correct labels
9. 9.True or false: “testing on training data” is good for training a model. (a) True (b) False
10. 10.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
11. 11.True or false: “many varied examples” is good for training a model. (a) False (b) True
12. 12.🏷️ 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
13. 13.Which of these is bad for training a model? (a) labels added at random (b) clear, sharp examples (c) many varied examples (d) photos in many lights
14. 14.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
15. 15.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
16. 16.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will turn yellow (c) it will shut down (d) it may get it wrong
17. 17.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) False (b) True
18. 18.Which of these is bad for training a model? (a) examples of every type (b) only one kind of example (c) many varied examples (d) new data for testing
19. 19.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
20. 20.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
21. 21.True or false: “photos in many lights” is good for training a model. (a) False (b) True
22. 22.True or false: “labels added at random” is good for training a model. (a) False (b) True
23. 23.🔍 Which is the odd one out? (a) examples of every type (b) clear, sharp examples (c) photos in many lights (d) testing on training data
24. 24.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
25. 25.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) False (b) True
26. 26.🔍 Which is the odd one out? (a) labels added at random (b) copies of one photo (c) correct labels (d) testing on training data
27. 27.🔍 Which is the odd one out? (a) testing on training data (b) blurry, unclear photos (c) checking labels twice (d) very few examples
28. 28.🏷️ 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) not spam (b) spam
29. 29.🏷️ 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
30. 30.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) testing on training data (d) checking labels twice
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) spam (b) not spam
32. 32.🔍 Which is the odd one out? (a) clear, sharp examples (b) copies of one photo (c) very few examples (d) labels added at random
33. 33.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
34. 34.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
35. 35.True or false: “wrong labels” is good for training a model. (a) True (b) False
36. 36.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
37. 37.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
38. 38.Which of these is good for training a model? (a) many varied examples (b) very few examples (c) testing on training data (d) wrong labels
39. 39.True or false: “clear, sharp examples” is bad for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training

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