Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 273 · 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.True or false: “labels added at random” is good for training a model. (a) True (b) False
2. 2.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) new data for testing (d) very few examples
3. 3.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
4. 4.🐶 To teach a model to spot dogs in photos, what do you give it? (a) many labelled photos (b) one single photo (c) a song about dogs (d) a list of rules only
5. 5.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
6. 6.Which of these is bad for training a model? (a) checking labels twice (b) copies of one photo (c) many varied examples (d) photos in many lights
7. 7.📚 Good or bad for training a model: very few examples? (a) bad for training (b) good for training
8. 8.True or false: “only one kind of example” is bad for training a model. (a) False (b) True
9. 9.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
10. 10.🏷️ 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
11. 11.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
12. 12.🏷️ 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
13. 13.True or false: “correct labels” is good for training a model. (a) False (b) True
14. 14.Which of these is good for training a model? (a) copies of one photo (b) photos in many lights (c) very few examples (d) testing on training data
15. 15.🔍 Which is the odd one out? (a) correct labels (b) photos in many lights (c) very few examples (d) clear, sharp examples
16. 16.🔍 Which is the odd one out? (a) testing on training data (b) many varied examples (c) photos in many lights (d) correct labels
17. 17.True or false: the message “Match practice moved to 5 pm” should be labelled “not spam”. (a) False (b) True
18. 18.🥭 A model saw only ripe yellow mangoes. A raw green mango arrives. What may happen? (a) it may get it wrong (b) it will be perfect (c) it will shut down (d) it will turn yellow
19. 19.🏷️ What is a “label” in machine learning? (a) the font size (b) the price (c) a sticker on a laptop (d) the right answer tag
20. 20.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
21. 21.🔍 Which is the odd one out? (a) examples of every type (b) labels added at random (c) checking labels twice (d) clear, sharp examples
22. 22.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
23. 23.True or false: “examples of every type” is good for training a model. (a) True (b) False
24. 24.📚 Good or bad for training a model: correct labels? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) very few examples (d) testing on training data
26. 26.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
27. 27.Which of these is bad for training a model? (a) many varied examples (b) correct labels (c) wrong labels (d) examples of every type
28. 28.🔍 Which is the odd one out? (a) many varied examples (b) examples of every type (c) only one kind of example (d) checking labels twice
29. 29.📚 Usually, more good examples make a model… (a) forget everything (b) better at its task (c) change colour (d) slower to switch on
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “not spam”. (a) False (b) True
31. 31.🏷️ 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) spam (b) not spam
32. 32.True or false: “many varied examples” is good for training a model. (a) False (b) True
33. 33.True or false: “examples of every type” is bad for training a model. (a) True (b) False
34. 34.🧠 Machine learning is a way for computers to… (a) print pages (b) charge faster (c) clean screens (d) learn from data
35. 35.Which of these is bad for training a model? (a) checking labels twice (b) clear, sharp examples (c) photos in many lights (d) very few examples
36. 36.🔍 Which is the odd one out? (a) wrong labels (b) clear, sharp examples (c) examples of every type (d) new data for testing
37. 37.True or false: “new data for testing” is bad for training a model. (a) False (b) True
38. 38.Which of these is bad for training a model? (a) new data for testing (b) blurry, unclear photos (c) photos in many lights (d) many varied examples
39. 39.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
40. 40.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) correct labels (d) copies of one photo

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