Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 65 · 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) checking labels twice (c) labels added at random (d) correct labels
2. 2.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
3. 3.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
4. 4.🐶 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) one single photo (d) many labelled photos
5. 5.🧠 Machine learning is a way for computers to… (a) learn from data (b) print pages (c) charge faster (d) clean screens
6. 6.Which of these is bad for training a model? (a) photos in many lights (b) wrong labels (c) new data for testing (d) checking labels twice
7. 7.Which of these is bad for training a model? (a) very few examples (b) examples of every type (c) new data for testing (d) many varied examples
8. 8.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
9. 9.Which of these is good for training a model? (a) labels added at random (b) copies of one photo (c) many varied examples (d) very few examples
10. 10.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) slower to switch on (d) change colour
11. 11.🏷️ 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
12. 12.True or false: “correct labels” is good for training a model. (a) True (b) False
13. 13.True or false: “testing on training data” is good for training a model. (a) True (b) False
14. 14.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) many varied examples (d) only one kind of example
15. 15.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
16. 16.🔍 Which is the odd one out? (a) photos in many lights (b) labels added at random (c) blurry, unclear photos (d) testing on training data
17. 17.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
18. 18.🏷️ 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
19. 19.🏷️ 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
20. 20.True or false: “only one kind of example” is good for training a model. (a) True (b) False
21. 21.🔍 Which is the odd one out? (a) very few examples (b) many varied examples (c) clear, sharp examples (d) new data for testing
22. 22.🔍 Which is the odd one out? (a) many varied examples (b) new data for testing (c) photos in many lights (d) wrong labels
23. 23.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
24. 24.Which of these is good for training a model? (a) examples of every type (b) blurry, unclear photos (c) only one kind of example (d) wrong labels
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) testing on training data (d) labels added at random
26. 26.🏷️ 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
27. 27.Which of these is good for training a model? (a) checking labels twice (b) labels added at random (c) only one kind of example (d) testing on training data
28. 28.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
29. 29.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) True (b) False
30. 30.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
31. 31.Which of these is good for training a model? (a) wrong labels (b) copies of one photo (c) testing on training data (d) correct labels
32. 32.🏷️ What is a “label” in machine learning? (a) the price (b) the font size (c) the right answer tag (d) a sticker on a laptop
33. 33.🏷️ 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
34. 34.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) checking labels twice (d) copies of one photo
35. 35.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
36. 36.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
37. 37.True or false: “very few examples” is bad for training a model. (a) False (b) True
38. 38.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
39. 39.🏷️ 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
40. 40.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) blurry, unclear photos (d) labels added at random

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