Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 306 · 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) clear, sharp examples (b) wrong labels (c) many varied examples (d) checking labels twice
2. 2.Which of these is good for training a model? (a) clear, sharp examples (b) very few examples (c) testing on training data (d) wrong labels
3. 3.📚 Good or bad for training a model: testing on training data? (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.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) not spam (b) spam
6. 6.🏷️ 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
7. 7.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
8. 8.Which of these is good for training a model? (a) checking labels twice (b) testing on training data (c) blurry, unclear photos (d) labels added at random
9. 9.🔍 Which is the odd one out? (a) wrong labels (b) checking labels twice (c) copies of one photo (d) only one kind of example
10. 10.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
11. 11.🔍 Which is the odd one out? (a) clear, sharp examples (b) correct labels (c) checking labels twice (d) labels added at random
12. 12.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
13. 13.🔍 Which is the odd one out? (a) testing on training data (b) correct labels (c) photos in many lights (d) many varied examples
14. 14.🏷️ 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
15. 15.Which of these is bad for training a model? (a) photos in many lights (b) testing on training data (c) many varied examples (d) examples of every type
16. 16.True or false: “labels added at random” is bad for training a model. (a) True (b) False
17. 17.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
18. 18.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) correct labels (d) very few examples
19. 19.🏷️ 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
20. 20.🔍 Which is the odd one out? (a) photos in many lights (b) testing on training data (c) very few examples (d) wrong labels
21. 21.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
22. 22.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
23. 23.Which of these is good for training a model? (a) copies of one photo (b) many varied examples (c) very few examples (d) labels added at random
24. 24.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) False (b) True
25. 25.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) blurry, unclear photos (d) correct labels
26. 26.📚 Good or bad for training a model: many varied examples? (a) bad for training (b) good for training
27. 27.True or false: the message “Your library book is due on Monday” should be labelled “not spam”. (a) True (b) False
28. 28.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) only one kind of example (d) many varied examples
29. 29.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
30. 30.True or false: “very few examples” is good for training a model. (a) False (b) True
31. 31.Which of these is good for training a model? (a) wrong labels (b) new data for testing (c) blurry, unclear photos (d) only one kind of example
32. 32.True or false: the message “Happy birthday! See you at lunch” should be labelled “not spam”. (a) False (b) True
33. 33.🧠 Machine learning is a way for computers to… (a) charge faster (b) print pages (c) clean screens (d) learn from data
34. 34.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
35. 35.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
36. 36.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) True (b) False
37. 37.📚 Usually, more good examples make a model… (a) better at its task (b) change colour (c) forget everything (d) slower to switch on
38. 38.🏷️ 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
39. 39.🏷️ 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
40. 40.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) spam (b) not spam

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