Learning Machine Learning with Aston

STEM: I was absent Monday and Tuesday of this week as I was out sick so this journal is mainly going to be about my spring break and what I've done since the technical journal. For the technical stuff, I have begun to move towards a middle job area of helping around both with the q learning with Robert and Connie and our game code with Puja, Edmond, and Will. I helped with the debugging of the game with an example of us fixing the game crash when wrong inputs are given. Other work includes trying to develop the reward aspect of the Q learning algorithm. Because Will and Edmond are out for a robotics competition, I see myself working with Puja on the code more this week. Spring Break: Over spring break, I went to Italy with my family. We stopped in Rome, Florence, and Milan over the week! Rome was our first stop and we went to see the Colosseum and the Roman Forum which were very historical and impressive. We also visited the Pantheon and I was amazed by the large d...

Tic Tac Toe Data: We first planned on spending this two weeks looking into datasets for tic tac toe and processing it as a pre-trial for our dots and boxes. What we found and did though, I though was far more insightful. Initially, I was looking around for datasets and found from UVA a dataset for tic tac toe intermediate game states. We thought this could be something as the UCI dataset that we had first come across only gave endgame states. An image of the UVA dataset is posted below. Discussing with Mr. Lee, though, we could not decipher what the non-zero/one numbers represented and decided to move on looking, a sort of dead end. We inferred that -1 and 1 were X and O while 0 was blank. We would eventually later find another way to gather datasets (see Tic Tac Toe Q Learning below). Part of Tic Tac Toe intermediate game states data Tic Tac Toe Monte Carlo: While we had been looking into datasets, it turns out that Will was experimenting with the Monte Carlo search tre...

Genetic Algorithm: After briefly looking into SARSA learning, I decided to switch and explore genetic algorithms after receiving a research paper Connie sent me. In this paper, researchers who were teaching an AI to play dots and boxes explained that they had used a genetic algorithm to create their neural network. As a result, I looked into genetic algorithms and was quite excited. This was because I could really connect with the algorithm as it was based on biology that I have studied. The close comparison made this research more tangible to me and I felt that I could relate more to it. The algorithm is explained in my Concept Map video below but it is basically an algorithm that simulates natural selection to choose the most "fit" neural network.  Caltech Visit: We visited Caltech on Thursday though we were short on people. This was because Edmond and Will were at a Robotics competition and Puja was stuck in Chemistry. Nevertheless, Robert, Connie, and I talked w...

This past week, Robert and I were in Yosemite for the annual trip up to the national park. We were originally supposed to leave on Sunday but snow delayed us to Monday. Because of the heavy snow, the usual Crane Flats campsite was inaccessible so we stayed in Yosemite Valley at the Yosemite Lodge. While this was not our original plane, I actually enjoyed staying in the valley more than at Crane Flats because we were at the center of the many attractions. For example, out hotel was right in front of Yosemite Falls (pictured below). On our first day of activity, the Winter Survival group was split into three different groups and my group and I got together to pass out lunch and discuss our day plans. While waiting for our group counselors from Naturebridge, we played in the snow and had a snow ball fight. After our counselors arrived, we hiked over to Yosemite Falls. At the base of the trail, we went through Spider Cave first. This was a cave made from large rocks that fell from the...

Before going to Caltech, we had narrowed down our ideas to one ambitious and one simpler project. The ambitious one was the e-cigarette image recognition system and the simpler one was going to be the game like tic-tac-toe. Upon getting to Caltech, though, our selection was pretty much dismissed as we went through all of our ideas with the graduate students. This was because Dr. Hassibi had to be in a meeting. Because we received further commentary on the other ideas, the visit really had us back to square one.  Some things I learned at Caltech exposed possible areas of weakness in our project ideas. The grad students pointed out that for the e-cigarette idea, we would need a lot of data, meaning that we would need a lot of examples of someone vaping in front of the thermal sensor. Robert explained that he saw a person train the image recognition algorithm with just 25 images. While the certainty wasn't great, the answer was typically correct. Because we are focusing on th...

After our visit to CalTech last Thursday, we narrowed down our semester 2 project ideas to the four that will be elaborated upon below. Before I do that, here's what I have been doing since last Thursday. First, I have been looking into current e-cigarette vapor sensors and how thermal imaging recognition works. I have also discussed the different project ideas with group mates and am working with Robert to focus on this more ambitious project. I am looking forward to exploring how to gather a dataset of thermal images for algorithm training and how effective our sensor would be. Idea 1: E-Cigarette Thermal Sensor Alert System One major issue that afflicts the youth of today is the use of e-cigarettes, wiping away remarkable progress in fighting smoking. This rise not only creates another issue for health professionals but issues in a new wave of modernization in our response to high-tech nicotine. Due to privacy laws, most students use use e-cigarettes in school restrooms and...

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 le...