Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 129 · 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 is the odd one out? (a) very few examples (b) blurry, unclear photos (c) clear, sharp examples (d) only one kind of example
2. 2.🔍 Which is the odd one out? (a) examples of every type (b) many varied examples (c) only one kind of example (d) photos in many lights
3. 3.Which of these is bad for training a model? (a) new data for testing (b) photos in many lights (c) correct labels (d) labels added at random
4. 4.📚 Usually, more good examples make a model… (a) better at its task (b) forget everything (c) change colour (d) slower to switch on
5. 5.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
6. 6.Which of these is good for training a model? (a) testing on training data (b) examples of every type (c) copies of one photo (d) only one kind of example
7. 7.True or false: “copies of one photo” is bad for training a model. (a) True (b) False
8. 8.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) checking labels twice (d) very few examples
9. 9.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
10. 10.Which of these is good for training a model? (a) new data for testing (b) copies of one photo (c) blurry, unclear photos (d) only one kind of example
11. 11.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
12. 12.🏷️ 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
13. 13.🏷️ 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
14. 14.🔍 Which is the odd one out? (a) labels added at random (b) very few examples (c) photos in many lights (d) copies of one photo
15. 15.🏷️ 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
16. 16.True or false: “correct labels” is good for training a model. (a) True (b) False
17. 17.True or false: “photos in many lights” is good for training a model. (a) True (b) False
18. 18.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
19. 19.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
20. 20.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
21. 21.True or false: “very few examples” is bad for training a model. (a) True (b) False
22. 22.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) spam (b) not spam
23. 23.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
24. 24.Which of these is good for training a model? (a) many varied examples (b) labels added at random (c) wrong labels (d) copies of one photo
25. 25.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
26. 26.True or false: “examples of every type” is bad for training a model. (a) False (b) True
27. 27.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
28. 28.True or false: “labels added at random” is good for training a model. (a) False (b) True
29. 29.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) correct labels (d) wrong labels
30. 30.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) many varied examples (d) wrong labels
31. 31.🏷️ 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
32. 32.Which of these is bad for training a model? (a) copies of one photo (b) correct labels (c) photos in many lights (d) new data for testing
33. 33.🥭 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
34. 34.Which of these is bad for training a model? (a) only one kind of example (b) many varied examples (c) checking labels twice (d) examples of every type
35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
36. 36.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
37. 37.Which of these is good for training a model? (a) blurry, unclear photos (b) checking labels twice (c) testing on training data (d) very few examples
38. 38.🔍 Which is the odd one out? (a) photos in many lights (b) clear, sharp examples (c) checking labels twice (d) very few examples
39. 39.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) testing on training data (b) examples of every type (c) photos in many lights (d) many varied examples

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