Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 315 · 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) correct labels (b) many varied examples (c) examples of every type (d) labels added at random
2. 2.🏷️ 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
3. 3.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
4. 4.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
5. 5.🐶 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
6. 6.Which of these is good for training a model? (a) wrong labels (b) examples of every type (c) copies of one photo (d) testing on training data
7. 7.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) the font size (d) a sticker on a laptop
8. 8.Which of these is bad for training a model? (a) correct labels (b) many varied examples (c) checking labels twice (d) wrong labels
9. 9.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) True (b) False
10. 10.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) wrong labels (d) correct labels
11. 11.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
12. 12.🏷️ 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
13. 13.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will turn yellow (c) it will be perfect (d) it will shut down
14. 14.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
15. 15.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
16. 16.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
17. 17.True or false: “new data for testing” is bad for training a model. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) wrong labels (b) blurry, unclear photos (c) clear, sharp examples (d) labels added at random
19. 19.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
20. 20.🔍 Which is the odd one out? (a) new data for testing (b) testing on training data (c) correct labels (d) clear, sharp examples
21. 21.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your account is locked, pay ₹99 now” (a) not spam (b) spam
22. 22.True or false: “photos in many lights” is good for training a model. (a) True (b) False
23. 23.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
24. 24.🔍 Which is the odd one out? (a) only one kind of example (b) checking labels twice (c) new data for testing (d) photos in many lights
25. 25.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
27. 27.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
28. 28.🏷️ 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
29. 29.🔍 Which is the odd one out? (a) labels added at random (b) wrong labels (c) correct labels (d) blurry, unclear photos
30. 30.True or false: “examples of every type” is good for training a model. (a) False (b) True
31. 31.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
32. 32.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
33. 33.Which of these is bad for training a model? (a) many varied examples (b) blurry, unclear photos (c) new data for testing (d) checking labels twice
34. 34.True or false: “checking labels twice” is good for training a model. (a) True (b) False
35. 35.Which of these is good for training a model? (a) labels added at random (b) blurry, unclear photos (c) many varied examples (d) testing on training data
36. 36.True or false: “photos in many lights” is bad for training a model. (a) False (b) True
37. 37.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) spam (b) not spam
38. 38.Which of these is good for training a model? (a) copies of one photo (b) correct labels (c) wrong labels (d) very few examples
39. 39.🏷️ 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
40. 40.📚 Usually, more good examples make a model… (a) change colour (b) slower to switch on (c) better at its task (d) forget everything

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