Stacks

In this lesson, students explore facial recognition technology as a “classification task.” Each time a smartphone uses facial recognition, it asks, “Is this me or not me?” Building on lesson 1, where students learned that images are numerical representations of pixel values, this lesson helps students visualize images in multidimensional space, with dimensions equal to the number of facial variables or features in the model.

This lesson introduces high school students to facial recognition and machine learning in statistics. Students will explore how images serve as quantitative data and learn about facial recognition through image comparison, similarity, and error thresholds.

In this lesson, students will gain a general overview of various Large Language Models (LLMs), their functions, and their applications in everyday life. By the end of the lesson, they will be able to explain what an LLM is, recognize its uses, and identify some of the popular LLMs currently available.

In this lesson, students start to understand the possible consequences of algorithmic bias. They consider a range of applications for which algorithmic bias presents a problem. This could be the 2nd lesson of a 2 part sequence.