Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 316 · 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) only one kind of example (b) testing on training data (c) copies of one photo (d) clear, sharp examples
2. 2.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) many labelled photos (c) a song about dogs (d) a list of rules only
3. 3.🏷️ 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
4. 4.True or false: “very few examples” is good for training a model. (a) False (b) True
5. 5.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) clear, sharp examples (d) wrong labels
6. 6.🔍 Which is the odd one out? (a) blurry, unclear photos (b) checking labels twice (c) wrong labels (d) only one kind of example
7. 7.Which of these is bad for training a model? (a) correct labels (b) new data for testing (c) many varied examples (d) copies of one photo
8. 8.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
9. 9.🏷️ 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
10. 10.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it will be perfect (b) it will shut down (c) it may get it wrong (d) it will turn yellow
11. 11.🔍 Which is the odd one out? (a) photos in many lights (b) wrong labels (c) blurry, unclear photos (d) copies of one photo
12. 12.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
13. 13.Which of these is good for training a model? (a) many varied examples (b) blurry, unclear photos (c) wrong labels (d) only one kind of example
14. 14.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
15. 15.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
16. 16.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
17. 17.Which of these is bad for training a model? (a) clear, sharp examples (b) photos in many lights (c) examples of every type (d) labels added at random
18. 18.🏷️ What is a “label” in machine learning? (a) a sticker on a laptop (b) the right answer tag (c) the font size (d) the price
19. 19.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
20. 20.🏷️ 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
21. 21.📚 Good or bad for training a model: blurry, unclear photos? (a) good for training (b) bad for training
22. 22.📚 Usually, more good examples make a model… (a) change colour (b) better at its task (c) slower to switch on (d) forget everything
23. 23.🏷️ 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
24. 24.🔍 Which is the odd one out? (a) many varied examples (b) checking labels twice (c) photos in many lights (d) labels added at random
25. 25.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) many varied examples (d) very few examples
26. 26.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
27. 27.True or false: the message “Last chance! Free gift card inside” should be labelled “spam”. (a) False (b) True
28. 28.Which of these is bad for training a model? (a) many varied examples (b) clear, sharp examples (c) very few examples (d) examples of every type
29. 29.Which of these is good for training a model? (a) only one kind of example (b) blurry, unclear photos (c) wrong labels (d) new data for testing
30. 30.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Last chance! Free gift card inside” (a) not spam (b) spam
31. 31.📚 Good or bad for training a model: only one kind of example? (a) bad for training (b) good for training
32. 32.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
33. 33.🔍 Which is the odd one out? (a) copies of one photo (b) only one kind of example (c) very few examples (d) examples of every type
34. 34.True or false: “new data for testing” is good for training a model. (a) False (b) True
35. 35.True or false: “clear, sharp examples” is good for training a model. (a) False (b) True
36. 36.Which of these is bad for training a model? (a) correct labels (b) photos in many lights (c) blurry, unclear photos (d) clear, sharp examples
37. 37.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
38. 38.Which of these is good for training a model? (a) very few examples (b) testing on training data (c) clear, sharp examples (d) wrong labels
39. 39.True or false: “checking labels twice” is bad for training a model. (a) True (b) False
40. 40.🔍 Which is the odd one out? (a) testing on training data (b) checking labels twice (c) photos in many lights (d) correct labels

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