Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 390 · 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.📚 Usually, more good examples make a model… (a) change colour (b) forget everything (c) better at its task (d) slower to switch on
2. 2.Which of these is good for training a model? (a) photos in many lights (b) copies of one photo (c) testing on training data (d) only one kind of example
3. 3.True or false: “new data for testing” is good for training a model. (a) False (b) True
4. 4.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
5. 5.🔍 Which is the odd one out? (a) new data for testing (b) correct labels (c) wrong labels (d) many varied examples
6. 6.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will shut down (b) it will turn yellow (c) it will be perfect (d) it may get it wrong
7. 7.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the price (c) the font size (d) the right answer tag
8. 8.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a song about dogs (c) many labelled photos (d) a list of rules only
9. 9.🏷️ 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
10. 10.True or false: “blurry, unclear photos” is good for training a model. (a) False (b) True
11. 11.🔍 Which is the odd one out? (a) only one kind of example (b) labels added at random (c) many varied examples (d) copies of one photo
12. 12.Which of these is bad for training a model? (a) many varied examples (b) blurry, unclear photos (c) checking labels twice (d) correct labels
13. 13.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
14. 14.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
15. 15.Which of these is bad for training a model? (a) correct labels (b) checking labels twice (c) examples of every type (d) labels added at random
16. 16.Which of these is good for training a model? (a) only one kind of example (b) copies of one photo (c) many varied examples (d) labels added at random
17. 17.True or false: “copies of one photo” is bad for training a model. (a) False (b) True
18. 18.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
19. 19.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
20. 20.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “You won a free phone! Click now!” (a) not spam (b) spam
21. 21.True or false: “correct labels” is good for training a model. (a) False (b) True
22. 22.True or false: “wrong labels” is good for training a model. (a) False (b) True
23. 23.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
24. 24.🏷️ 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
25. 25.🔍 Which is the odd one out? (a) labels added at random (b) photos in many lights (c) new data for testing (d) clear, sharp examples
26. 26.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) copies of one photo (d) correct labels
27. 27.Which of these is bad for training a model? (a) copies of one photo (b) many varied examples (c) clear, sharp examples (d) checking labels twice
28. 28.🔍 Which is the odd one out? (a) labels added at random (b) new data for testing (c) only one kind of example (d) testing on training data
29. 29.Which of these is bad for training a model? (a) many varied examples (b) examples of every type (c) very few examples (d) photos in many lights
30. 30.True or false: “examples of every type” is bad for training a model. (a) False (b) True
31. 31.Which of these is good for training a model? (a) only one kind of example (b) very few examples (c) testing on training data (d) checking labels twice
32. 32.True or false: “correct labels” is bad for training a model. (a) False (b) True
33. 33.📚 Good or bad for training a model: correct labels? (a) good for training (b) bad for training
34. 34.🔍 Which is the odd one out? (a) checking labels twice (b) examples of every type (c) only one kind of example (d) photos in many lights
35. 35.🔍 Which is the odd one out? (a) blurry, unclear photos (b) testing on training data (c) checking labels twice (d) wrong labels
36. 36.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) False (b) True
37. 37.Which of these is good for training a model? (a) only one kind of example (b) testing on training data (c) clear, sharp examples (d) copies of one photo
38. 38.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) not spam (b) spam
39. 39.🔍 Which is the odd one out? (a) labels added at random (b) correct labels (c) copies of one photo (d) testing on training data
40. 40.🔍 Which is the odd one out? (a) examples of every type (b) only one kind of example (c) labels added at random (d) blurry, unclear photos

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