Developing effective high school pedagogy for machine learning

The data we have at our disposal is massive, and for years industry has been at a loss for how to process the data we have into a meaningful story. Machine learning is a crucial method that has recently been changing how we interpret that data, making more efficient and informed products and services. It’s important to develop effective pedagogical methods for creating the next generation of computer scientists who will utilize and expand upon these tools. In order to do this, we looked at introductory collegiate-level classes in Machine Learning and Aurelien Geron’s Hands-On Machine Learning with Scikit-Learn & Tensor Flow as guidelines for what to include. In creating the elective, we attempt to follow the same structure as these college courses while keeping the information accessible at a high-school level. As a seminar class, the course also includes a degree of self-guided exploration into deeper topics. Currently, we have conducted enough research to formulate a preliminary lesson plan for next year’s Machine Learning Seminar Elective. We are further developing our lesson plan with numerous activities and projects to ensure the students understand the concepts covered in the elective