Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 264 · 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.🏷️ 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
2. 2.🔍 Which is the odd one out? (a) correct labels (b) checking labels twice (c) photos in many lights (d) testing on training data
3. 3.True or false: the message “Last chance! Free gift card inside” should be labelled “not spam”. (a) True (b) False
4. 4.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “spam”. (a) False (b) True
5. 5.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Send your password to claim a prize” (a) spam (b) not spam
6. 6.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) the price (c) a sticker on a laptop (d) the font size
7. 7.True or false: the message “You won a free phone! Click now!” should be labelled “not spam”. (a) False (b) True
8. 8.📚 Good or bad for training a model: labels added at random? (a) bad for training (b) good for training
9. 9.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
10. 10.Which of these is good for training a model? (a) labels added at random (b) very few examples (c) blurry, unclear photos (d) photos in many lights
11. 11.🔍 Which is the odd one out? (a) photos in many lights (b) blurry, unclear photos (c) examples of every type (d) many varied examples
12. 12.True or false: “wrong labels” is good for training a model. (a) True (b) False
13. 13.🔍 Which is the odd one out? (a) checking labels twice (b) many varied examples (c) only one kind of example (d) new data for testing
14. 14.True or false: the message “You won a free phone! Click now!” should be labelled “spam”. (a) False (b) True
15. 15.🔍 Which is the odd one out? (a) examples of every type (b) testing on training data (c) very few examples (d) copies of one photo
16. 16.🏷️ 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
17. 17.True or false: the message “Please buy milk on the way home” should be labelled “spam”. (a) True (b) False
18. 18.🔍 Which is the odd one out? (a) labels added at random (b) checking labels twice (c) testing on training data (d) wrong labels
19. 19.🔍 Which is the odd one out? (a) wrong labels (b) only one kind of example (c) testing on training data (d) many varied examples
20. 20.True or false: the message “Share your OTP to get cashback” should be labelled “spam”. (a) True (b) False
21. 21.📚 Usually, more good examples make a model… (a) slower to switch on (b) forget everything (c) better at its task (d) change colour
22. 22.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
23. 23.True or false: the message “The class picnic is on Friday at 8 am” should be labelled “not spam”. (a) True (b) False
24. 24.📚 Good or bad for training a model: new data for testing? (a) bad for training (b) good for training
25. 25.🔍 Which is the odd one out? (a) copies of one photo (b) many varied examples (c) checking labels twice (d) photos in many lights
26. 26.Which of these is good for training a model? (a) very few examples (b) new data for testing (c) only one kind of example (d) blurry, unclear photos
27. 27.🔍 Which is the odd one out? (a) testing on training data (b) very few examples (c) clear, sharp examples (d) labels added at random
28. 28.Which of these is bad for training a model? (a) testing on training data (b) examples of every type (c) photos in many lights (d) many varied examples
29. 29.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Your library book is due on Monday” (a) not spam (b) spam
30. 30.True or false: the message “Send your password to claim a prize” should be labelled “spam”. (a) False (b) True
31. 31.🧠 Machine learning is a way for computers to… (a) charge faster (b) clean screens (c) learn from data (d) print pages
32. 32.🏷️ 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
33. 33.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Science project groups are on the board” (a) not spam (b) spam
34. 34.Which of these is good for training a model? (a) wrong labels (b) very few examples (c) testing on training data (d) many varied examples
35. 35.📚 Good or bad for training a model: examples of every type? (a) good for training (b) bad for training
36. 36.True or false: “examples of every type” is bad for training a model. (a) True (b) False
37. 37.True or false: the message “Happy birthday! See you at lunch” should be labelled “spam”. (a) True (b) False
38. 38.📚 Good or bad for training a model: checking labels twice? (a) good for training (b) bad for training
39. 39.📚 Good or bad for training a model: copies of one photo? (a) bad for training (b) good for training
40. 40.🐶 To teach a model to spot dogs in photos, what do you give it? (a) one single photo (b) a list of rules only (c) a song about dogs (d) many labelled photos

Free ai for students lessons, practice and worksheets at talentjr.in/ai-for-students