Abstract
We live in a time which is witnessing a boom in the field of Computer Science especially in the subfields of Machine Learning and Artificial Intelligence. Yet, there is a noticeable gap in the number of qualified job applicants who can help solve some of the Machine Learning problems of the day. There is a current movement to introduce Computer Science education much earlier, to k-12 students. The hope is, with an earlier introduction and better continuous training, students will be properly trained and interested into studying Computer Science in college and able to obtain these coveted positions after they graduate. Given this hope and movement, there needs to be more applications and instructional plans to support k-12 teachers as they bring more Computer Science topics into the classroom. One such area of Computer Science is Machine Learning. Currently there are some tools and curriculum available for broader Computer Science areas. But there is a lack of instructional methods and tools for Machine Learning for High School Students. And there is especially a lack of visually interactive lesson plans for high schoolers to learn basic concepts in Machine Learning. To solve this problem, I present a visually interactive instructional tool intended to introduce basic concepts of Machine Learning to High School Students. Studies have shown that any content when delivered in form that is visual and interactive along with textual information is more likely to be retained in memory. The tool is interactive and also focuses on giving the High School Students a taste of required mathematical background for Machine Leaning and Artificial Intelligence. Selection of datasets and problem I am trying to solve are chosen such that the High School Students can relate to them, for example instead of solving problems like stock price prediction and network intrusion detection, I will solve problems like Rain and No Rain Prediction and Temperature Prediction, which can be more interesting for High School Students. The students are more likely to stay focused and interested if they have a feeling of achievement after completing a topic. They start with simpler yet interesting problems and work their way up. Through this tool a student will be able to brush up basic concepts of mathematics required for Machine Learning and Artificial Intelligence and classical Machine Learning and Artificial Intelligence algorithms like K Nearest Neighbors, Basic Linear Classification, Linear Regression, Naïve Bayes and others in more interactive ways through the use of visualizations such as graphs and charts.