Machine Learning for Kids Worksheet

AI for students lesson 2: How machines learn from examples · Set 21 · 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 of these is bad for training a model? (a) photos in many lights (b) clear, sharp examples (c) testing on training data (d) correct labels
2. 2.🔍 Which is the odd one out? (a) correct labels (b) copies of one photo (c) testing on training data (d) very few examples
3. 3.Which of these is good for training a model? (a) very few examples (b) correct labels (c) wrong labels (d) copies of one photo
4. 4.📚 Good or bad for training a model: photos in many lights? (a) good for training (b) bad for training
5. 5.🔍 Which is the odd one out? (a) new data for testing (b) blurry, unclear photos (c) correct labels (d) checking labels twice
6. 6.🏷️ You are labelling messages to train a spam filter. How should this one be labelled? “Happy birthday! See you at lunch” (a) spam (b) not spam
7. 7.📚 Good or bad for training a model: very few examples? (a) good for training (b) bad for training
8. 8.True or false: “correct labels” is good for training a model. (a) False (b) True
9. 9.🔍 Which is the odd one out? (a) checking labels twice (b) testing on training data (c) examples of every type (d) new data for testing
10. 10.True or false: the message “Science project groups are on the board” should be labelled “not spam”. (a) True (b) False
11. 11.🔍 Which is the odd one out? (a) only one kind of example (b) very few examples (c) labels added at random (d) many varied examples
12. 12.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) only one kind of example (d) clear, sharp examples
13. 13.🔍 Which is the odd one out? (a) new data for testing (b) photos in many lights (c) many varied examples (d) labels added at random
14. 14.🏷️ 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
15. 15.🏷️ 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
16. 16.🔍 Which is the odd one out? (a) blurry, unclear photos (b) very few examples (c) testing on training data (d) checking labels twice
17. 17.📚 Good or bad for training a model: wrong labels? (a) good for training (b) bad for training
18. 18.True or false: “very few examples” is bad for training a model. (a) True (b) False
19. 19.📚 Good or bad for training a model: clear, sharp examples? (a) bad for training (b) good for training
20. 20.🔍 Which is the odd one out? (a) clear, sharp examples (b) copies of one photo (c) wrong labels (d) only one kind of example
21. 21.Which of these is good for training a model? (a) testing on training data (b) copies of one photo (c) only one kind of example (d) examples of every type
22. 22.True or false: “photos in many lights” is good for training a model. (a) False (b) True
23. 23.🏷️ 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
24. 24.True or false: “copies of one photo” is good for training a model. (a) False (b) True
25. 25.True or false: “examples of every type” is good for training a model. (a) False (b) True
26. 26.📚 Good or bad for training a model: blurry, unclear photos? (a) bad for training (b) good for training
27. 27.🏷️ What is a “label” in machine learning? (a) the right answer tag (b) a sticker on a laptop (c) the font size (d) the price
28. 28.📚 Usually, more good examples make a model… (a) forget everything (b) change colour (c) slower to switch on (d) better at its task
29. 29.📚 Good or bad for training a model: many varied examples? (a) good for training (b) bad for training
30. 30.True or false: the message “Science project groups are on the board” should be labelled “spam”. (a) False (b) True
31. 31.🏷️ 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
32. 32.📚 Good or bad for training a model: checking labels twice? (a) bad for training (b) good for training
33. 33.True or false: the message “Your library book is due on Monday” should be labelled “spam”. (a) True (b) False
34. 34.🧠 Machine learning is a way for computers to… (a) clean screens (b) print pages (c) learn from data (d) charge faster
35. 35.🏷️ 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
36. 36.True or false: “correct labels” is bad for training a model. (a) False (b) True
37. 37.Which of these is bad for training a model? (a) photos in many lights (b) labels added at random (c) many varied examples (d) clear, sharp examples
38. 38.True or false: the message “Your account is locked, pay ₹99 now” should be labelled “not spam”. (a) True (b) False
39. 39.🔍 Which is the odd one out? (a) testing on training data (b) wrong labels (c) very few examples (d) photos in many lights
40. 40.Which of these is good for training a model? (a) very few examples (b) blurry, unclear photos (c) testing on training data (d) checking labels twice

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