G12 ML with teaching Q

Multiple Choice

Which of the following is a real-life application of image recognition technology?

• Detecting emotions in photos

• Writing essays

• Solving math problems

• Playing musical instruments

Open Ended

What is the significance of teaching computers to detect emotions using image recognition?

Open Ended

What are the success criteria for training a machine learning model using Google Teachable Machine?

Multiple Select

Which of the following are real-life examples of supervised learning?

• Email Spam Detection

• Customer Segmentation

• Weather Forecasting

• Student Grade Prediction

Multiple Choice

Which type of machine learning uses labeled data to train models and predict future outcomes?

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Deep Learning

Multiple Choice

Which type of machine learning is used for grouping customers by purchasing behavior and finding patterns in data without predefined outputs?

• Supervised Learning

• Unsupervised Learning

• Reinforcement Learning

• Transfer Learning

Open Ended

Explain the difference between labeled data and unlabeled data in the context of machine learning.

Fill in the Blank

In reinforcement learning, the machine learns by ___ and error through rewards and punishments.

Open Ended

Explain how feedback is used in reinforcement learning to improve the performance of self-driving cars.

Multiple Choice

Which of the following is a key characteristic of reinforcement learning?

• Learning from labeled data

• Learning by trial and error through rewards and punishments

• Learning without any feedback

• Learning by memorizing examples

Fill in the Blank

Fill in the blank: To test your image detection model in Google Teachable Machine, you need to use your ___.

Multiple Choice

Arrange the steps involved in building an image detection model using Google Teachable Machine in the correct order: Collect and label images, Test the model, Train the model, Improve the model.

• Collect and label images → Train the model → Test the model → Improve the model

• Train the model → Collect and label images → Test the model → Improve the model

• Test the model → Collect and label images → Train the model → Improve the model

• Improve the model → Collect and label images → Train the model → Test the model

Open Ended

Discuss the differences between labeled and unlabeled data in the context of training a teachable machine model.

Multiple Select

Which of the following are important for achieving high accuracy in a teachable machine model for identifying tree types?

• Using high-quality labeled images

• Ensuring balanced data for each class

• Testing the model and reflecting on improvements

• Using unlabeled images

Multiple Choice

Which of the following is NOT typically included in the 'Teacher Corner' section of a lesson plan?

• Standard

• Differentiation

• Student grades

• Quiz links

Fill in the Blank

AI learns from labeled data to create an image detection model using ___ Teachable Machine.

Open Ended

Reflecting on today's lesson about image detection models and AI, what is one real-world application of image recognition that you found most interesting or relevant?

Open Ended

Explain how the 'I DO, We DO, You DO together, You DO alone' instructional model can be integrated with assessment strategies such as Exit Tickets and Peer Assessment to support student learning.