Technical Journal: Sem 1 Reflection & Sem 2 Project Introduction

My first semester of Caltech STEM Research really exposed me to a lot of more in-depth concepts that I have come to really appreciate. Such concepts include the wide range of clustering algorithms, decision trees, recommender systems, and more. It felt very fulfilling to be exploring machine learning in a high school environment. For example, the iris decision tree project (depicted below) was quite different from my past interactions with computer science. We were actually making real-world deductions from the iris dataset and not just coding a game of battleship. The explorations of these concepts also enabled me to get to know my machine learning group mates better and working with them has also made the class a lot more fun and collaborative.



A very useful skill I learned last semester was the screen capture video recording. This key aspect of my concept maps proved useful in creating a video in my Economics class and sending a video to Naviance regarding an issue. I also learned how much math was involved in machine learning. Before, I had expected machine learning and computer science to be much more about the code. Learning about the friendship paradox and linear predictors, for example though, I realized how certain math concepts were actually very applicable in machine learning. Situations regarding dimensionality in data also made me appreciate how computers can interpret these higher dimensional datasets that I could not.



Another major part of my last semester was my paper airplane project. I learned a lot about how research and its steps work from introduction to methodology to discussion. The structure that this semester long assignment provided really cleared up to me what I will be aiming for this semester. The paper airplane project also taught me how to utilize spreadsheets for data calculations and taught me quite a lot about the aerodynamics of paper airplanes. Working with Elizabeth also enabled me to collaborate with more people and the end report provided a taste as to what the machine learning group and I could possibly achieve.



One thing I also learned this past semester was how self-reliant research was. First, I had and still find some challenge in translating my past Python experiences with applications now. While Codecadamy and also the Runestone lessons were more directed and point by point, the particular uncertainty of coding algorithms from scratch seemed a bit more daunting. Going through many different websites, online course videos, and research papers also showed me how much information there is out there. Our side-project with the student survey asking some random question (like toilet paper under or over?) also taught me how receptive our teachers and school were to providing us with data. Concept Maps also taught me how to best simplify and teach the concepts I was exploring for others.

Semester 2 Project Introduction: (one idea from group's brainstorming)
In our project, we will use machine learning to create a program that will make food recommendations for you based on your past food choices. There are many food options out there with multitudes of restaurants so we are trying to answer quickly the common question of "what should we eat?" This abundance of options oftentimes result in time spent making the decisions and also mistakes in decisions by choosing more convenient though less nutritious foods. In order to save time and promote healthy choices, our project will try to improve people's lifestyles and make life more efficient. We plan on accomplishing this by training the program with people's past food choices and the frequency with which they are eaten. We think the machine learning will enable the program to find possible patterns and/or habits that enable it to make recommendations. We will then test the program by comparing the predicted foods with the person's actual choices in real life. These recommendations would be based on food favorability and nutrition. Our results can hopefully empower people to make smarter decisions while eating and also save people time from thinking about what "mood" they are in for dinner. 

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