Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 502 · 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.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
2. 2.🏷️ 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
3. 3.📚 Good or bad for training a model: copies of one photo? (a) good for training (b) bad for training
4. 4.True or false: “copies of one photo” is good for training a model. (a) True (b) False
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Match practice moved to 5 pm” (a) not spam (b) spam
6. 6.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
7. 7.📚 Usually, more good examples make a model… (a) slower to switch on (b) better at its task (c) forget everything (d) change colour
8. 8.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) copies of one photo (d) new data for testing
9. 9.True or false: “labels added at random” is good for training a model. (a) True (b) False
10. 10.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
11. 11.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
12. 12.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) labels added at random (d) photos in many lights
14. 14.🏷️ 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
15. 15.🔍 Which is the odd one out? (a) photos in many lights (b) examples of every type (c) correct labels (d) blurry, unclear photos
16. 16.Which of these is bad for training a model? (a) correct labels (b) copies of one photo (c) examples of every type (d) photos in many lights
17. 17.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) very few examples (d) copies of one photo
19. 19.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
20. 20.Which of these is bad for training a model? (a) clear, sharp examples (b) many varied examples (c) photos in many lights (d) very few examples
21. 21.🥭 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
22. 22.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a song about dogs (b) one single photo (c) a list of rules only (d) many labelled photos
23. 23.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
24. 24.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
25. 25.Which of these is good for training a model? (a) labels added at random (b) wrong labels (c) very few examples (d) photos in many lights
26. 26.🏷️ 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
27. 27.Which of these is good for training a model? (a) copies of one photo (b) only one kind of example (c) clear, sharp examples (d) wrong labels
28. 28.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) blurry, unclear photos (d) checking labels twice
29. 29.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
30. 30.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) many varied examples (b) copies of one photo (c) testing on training data (d) labels added at random
32. 32.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the right answer tag (d) the font size
33. 33.🔍 Which is the odd one out? (a) new data for testing (b) very few examples (c) photos in many lights (d) correct labels
34. 34.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) labels added at random (d) blurry, unclear photos
35. 35.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
36. 36.True or false: “correct labels” is good for training a model. (a) True (b) False
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
38. 38.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) new data for testing (d) copies of one photo
39. 39.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) labels added at random (b) photos in many lights (c) copies of one photo (d) very few examples

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