Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 527 · 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) examples of every type (c) many varied examples (d) only one kind of example
2. 2.🔍 Which is the odd one out? (a) labels added at random (b) blurry, unclear photos (c) testing on training data (d) examples of every type
3. 3.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “spam”. (a) True (b) False
4. 4.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) copies of one photo (d) very few examples
5. 5.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
6. 6.True or false: “new data for testing” is bad for training a model. (a) True (b) False
7. 7.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) False (b) True
8. 8.🏷️ 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
9. 9.True or false: the message “Share your OTP to get cashback” should be labelled “not spam”. (a) False (b) True
10. 10.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) change colour (d) better at its task
11. 11.True or false: “labels added at random” is good for training a model. (a) False (b) True
12. 12.🥭 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
13. 13.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
14. 14.🏷️ 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
15. 15.🔍 Which is the odd one out? (a) photos in many lights (b) correct labels (c) clear, sharp examples (d) very few examples
16. 16.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
17. 17.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a list of rules only (d) a song about dogs
18. 18.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
19. 19.Which of these is bad for training a model? (a) many varied examples (b) photos in many lights (c) wrong labels (d) new data for testing
20. 20.True or false: “examples of every type” is bad for training a model. (a) False (b) True
21. 21.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
22. 22.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
23. 23.True or false: “blurry, unclear photos” is good for training a model. (a) True (b) False
24. 24.Which of these is bad for training a model? (a) very few examples (b) photos in many lights (c) checking labels twice (d) many varied examples
25. 25.🏷️ What is a “label” in machine learning? (a) the font size (b) the right answer tag (c) the price (d) a sticker on a laptop
26. 26.🏷️ 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
27. 27.🔍 Which is the odd one out? (a) correct labels (b) wrong labels (c) blurry, unclear photos (d) very few examples
28. 28.True or false: “labels added at random” is bad for training a model. (a) False (b) True
29. 29.📚 Good or bad for training a model: wrong labels? (a) bad for training (b) good for training
30. 30.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
31. 31.🔍 Which is the odd one out? (a) correct labels (b) blurry, unclear photos (c) new data for testing (d) many varied examples
32. 32.Which of these is good for training a model? (a) wrong labels (b) testing on training data (c) many varied examples (d) very few examples
33. 33.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
34. 34.True or false: “very few examples” is good for training a model. (a) True (b) False
35. 35.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
36. 36.Which of these is good for training a model? (a) testing on training data (b) very few examples (c) labels added at random (d) photos in many lights
37. 37.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) True (b) False
38. 38.Which of these is bad for training a model? (a) many varied examples (b) copies of one photo (c) photos in many lights (d) correct labels
39. 39.True or false: “many varied examples” is bad for training a model. (a) True (b) False
40. 40.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training

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