Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 406 · 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: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
2. 2.🏷️ 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
3. 3.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) testing on training data (d) many varied examples
4. 4.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
5. 5.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) True (b) False
6. 6.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
7. 7.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
8. 8.True or false: “photos in many lights” is good for training a model. (a) False (b) True
9. 9.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
10. 10.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
11. 11.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
12. 12.Which of these is good for training a model? (a) wrong labels (b) blurry, unclear photos (c) copies of one photo (d) many varied examples
13. 13.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
14. 14.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) labels added at random (d) blurry, unclear photos
15. 15.🔍 Which is the odd one out? (a) clear, sharp examples (b) checking labels twice (c) testing on training data (d) many varied examples
16. 16.🧠 Machine learning is a way for computers to… (a) learn from data (b) charge faster (c) clean screens (d) print pages
17. 17.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
18. 18.Which of these is good for training a model? (a) blurry, unclear photos (b) wrong labels (c) very few examples (d) clear, sharp examples
19. 19.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) one single photo (c) a song about dogs (d) many labelled photos
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) new data for testing (c) very few examples (d) checking labels twice
21. 21.True or false: “many varied examples” is good for training a model. (a) True (b) False
22. 22.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) wrong labels (d) checking labels twice
23. 23.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) blurry, unclear photos (d) correct labels
24. 24.🥭 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
25. 25.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
26. 26.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
27. 27.Which of these is good for training a model? (a) blurry, unclear photos (b) very few examples (c) labels added at random (d) photos in many lights
28. 28.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) blurry, unclear photos (d) copies of one photo
29. 29.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
30. 30.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
31. 31.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
32. 32.🏷️ 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
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) testing on training data (d) correct labels
34. 34.Which of these is good for training a model? (a) checking labels twice (b) only one kind of example (c) testing on training data (d) blurry, unclear photos
35. 35.🔍 Which is the odd one out? (a) copies of one photo (b) labels added at random (c) only one kind of example (d) many varied examples
36. 36.🏷️ 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
37. 37.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) testing on training data (d) photos in many lights
39. 39.🏷️ 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
40. 40.Which of these is bad for training a model? (a) photos in many lights (b) correct labels (c) copies of one photo (d) checking labels twice

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