Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 388 · 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.📚 Good or bad for training a model: only one kind of example? (a) good for training (b) bad for training
2. 2.🔍 Which is the odd one out? (a) copies of one photo (b) correct labels (c) photos in many lights (d) new data for testing
3. 3.True or false: “only one kind of example” is good for training a model. (a) False (b) True
4. 4.Which of these is bad for training a model? (a) new data for testing (b) many varied examples (c) only one kind of example (d) clear, sharp examples
5. 5.🏷️ 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
6. 6.True or false: “very few examples” is good for training a model. (a) True (b) False
7. 7.🔍 Which is the odd one out? (a) clear, sharp examples (b) many varied examples (c) testing on training data (d) new data for testing
8. 8.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Please buy milk on the way home” (a) not spam (b) spam
9. 9.🔍 Which is the odd one out? (a) copies of one photo (b) blurry, unclear photos (c) many varied examples (d) labels added at random
10. 10.🔍 Which is the odd one out? (a) very few examples (b) correct labels (c) copies of one photo (d) wrong labels
11. 11.True or false: “checking labels twice” is good for training a model. (a) False (b) True
12. 12.🔍 Which is the odd one out? (a) very few examples (b) clear, sharp examples (c) photos in many lights (d) checking labels twice
13. 13.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) correct labels (d) testing on training data
14. 14.🐶 To teach a model to spot dogs in photos, what do you give it? (a) a list of rules only (b) a song about dogs (c) one single photo (d) many labelled photos
15. 15.Which of these is good for training a model? (a) only one kind of example (b) wrong labels (c) blurry, unclear photos (d) photos in many lights
16. 16.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) True (b) False
17. 17.True or false: the message “Earn ₹50,000 a day from home!!!” should be labelled “not spam”. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) photos in many lights (b) checking labels twice (c) clear, sharp examples (d) labels added at random
19. 19.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) not spam (b) spam
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) new data for testing (c) correct labels (d) blurry, unclear photos
21. 21.🔍 Which is the odd one out? (a) blurry, unclear photos (b) clear, sharp examples (c) testing on training data (d) very few examples
22. 22.🔍 Which is the odd one out? (a) many varied examples (b) photos in many lights (c) correct labels (d) wrong labels
23. 23.Which of these is bad for training a model? (a) examples of every type (b) many varied examples (c) photos in many lights (d) labels added at random
24. 24.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) new data for testing (b) many varied examples (c) examples of every type (d) only one kind of example
26. 26.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
27. 27.📚 Good or bad for training a model: examples of every type? (a) bad for training (b) good for training
28. 28.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) True (b) False
29. 29.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
30. 30.📚 Good or bad for training a model: new data for testing? (a) good for training (b) bad for training
31. 31.📚 Good or bad for training a model: photos in many lights? (a) bad for training (b) good for training
32. 32.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
33. 33.🥭 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
34. 34.True or false: the message “Please buy milk on the way home” should be labelled “not spam”. (a) False (b) True
35. 35.True or false: “correct labels” is good for training a model. (a) False (b) True
36. 36.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Share your OTP to get cashback” (a) spam (b) not spam
37. 37.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) False (b) True
38. 38.🏷️ 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
39. 39.Which of these is bad for training a model? (a) checking labels twice (b) very few examples (c) correct labels (d) many varied examples
40. 40.Which of these is bad for training a model? (a) photos in many lights (b) many varied examples (c) wrong labels (d) new data for testing

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