Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 103 · 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) labels added at random (c) examples of every type (d) many varied examples
2. 2.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
3. 3.True or false: “photos in many lights” is good for training a model. (a) True (b) False
4. 4.True or false: the message “Match practice moved to 5 pm” should be labelled “spam”. (a) True (b) False
5. 5.Which of these is good for training a model? (a) testing on training data (b) labels added at random (c) very few examples (d) checking labels twice
6. 6.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
7. 7.🔍 Which is the odd one out? (a) many varied examples (b) correct labels (c) copies of one photo (d) new data for testing
8. 8.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
9. 9.🐶 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
10. 10.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) new data for testing (b) examples of every type (c) wrong labels (d) photos in many lights
12. 12.True or false: “labels added at random” is bad for training a model. (a) False (b) True
13. 13.True or false: “wrong labels” is good for training a model. (a) False (b) True
14. 14.📚 Good or bad for training a model: testing on training data? (a) good for training (b) bad for training
15. 15.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
16. 16.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) True (b) False
17. 17.Which of these is bad for training a model? (a) checking labels twice (b) examples of every type (c) copies of one photo (d) new data for testing
18. 18.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) blurry, unclear photos (d) photos in many lights
19. 19.🏷️ What is a “label” in machine learning? (a) the price (b) the right answer tag (c) a sticker on a laptop (d) the font size
20. 20.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
21. 21.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it may get it wrong (c) it will turn yellow (d) it will be perfect
22. 22.Which of these is good for training a model? (a) very few examples (b) photos in many lights (c) labels added at random (d) only one kind of example
23. 23.📚 Good or bad for training a model: clear, sharp examples? (a) good for training (b) bad for training
24. 24.True or false: “correct labels” is good for training a model. (a) False (b) True
25. 25.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
26. 26.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
27. 27.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
28. 28.True or false: “correct labels” is bad for training a model. (a) True (b) False
29. 29.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) many varied examples (d) testing on training data
30. 30.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) False (b) True
31. 31.🔍 Which is the odd one out? (a) correct labels (b) labels added at random (c) many varied examples (d) new data for testing
32. 32.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) testing on training data (d) photos in many lights
33. 33.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
34. 34.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) spam (b) not spam
35. 35.🔍 Which is the odd one out? (a) copies of one photo (b) clear, sharp examples (c) testing on training data (d) labels added at random
36. 36.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) False (b) True
37. 37.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) True (b) False
38. 38.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
39. 39.True or false: “wrong labels” is bad for training a model. (a) False (b) True
40. 40.🔍 Which is the odd one out? (a) wrong labels (b) new data for testing (c) copies of one photo (d) blurry, unclear photos

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