Learning Machine Learning with Aston

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...

Dimensionality Reduction: Before going to Caltech, I finished up looking into dimensionality reduction for the recommender system concept map. My concept map video is linked below! I learned more about the different techniques for dimensionality reduction like PCA and low variance filters. I also explored the applications and found how it can be used to reduce data noise in images. It consequently makes the picture clearer. I thought it was quite satisfying to look into an aspect of the concept that enables everything else to work! CalTech: We visited Caltech on Thursday where we were introduced to linear predictors. They are sort of a variation of linear regression. Dr. Hassibi explained how we could use linear predictors to find an equation that uses our survey data. The equation would sum up the product of coefficients and input survey answers to get a value. A threshold value would then be set to determine where the final question is a yes or no. I also learned that whe...

Recommender Systems: After my last journal, I looked into prior to Thanksgiving break and am looking into now the topic of dimensionality reduction in the larger concept map of recommender systems. I found several videos with one linked below that were really helpful. I learned how dimensionality reduction is used to simplify and streamline the processing of the data. Using principle component analysis, dimensionality reduction is used to create a plane of lesser dimensions that contains all the data points at a higher dimension. I look forward to sharing what I have learned with the group, and I feel that dimensionality reduction will play an important part in ensuring the matrix processes run smoothly. Thanksgiving Break: time to relax With the start of break, I spent time finishing all schoolwork during the first weekend. My family and I went to pick up my sister at the airport and got Japanese soba in Torrance on the way back. I tried out a brunch cafe near my home that I...

On our visit last Thursday to Caltech, the group and I meet with graduate student Ethan and discussed recommender systems. Recommender systems are used all around us from Youtube to Spotify but we focused on the Netflix recommender system as Netflix actually has datasets published. Ethan told us that this dataset is out there because Netflix is challenging people to develop an even better recommender system than their current one using that data. He added that there was a prize of $1 million dollars so that also added some extra incentive. The dataset that is used to make the recommender system is similar to that pictured above with a matrix created by users and their ratings of movies that they have watched. The empty spaces are the movies that users have not watched yet and we are trying to solve which blank spaced movies should be recommended to the user. After discussing, we explored three ways to break down the matrix and make recommendations. The first is similarity c...

Concept Maps:  Finishing off my Concept Map on k-means  algorithm, I explained more about k-means to the rest of my group while we all shared information about what we learned. I created my Concept Map video on the k-means algorithm (pasted below) while the group and I discussed how centrality worked in data graphing (pictured below). While we were originally planning on focussing in on centrality for our next concept map, our visit to Caltech changed the course of our plans as centrality was not as focussed upon. Will had figured out parts of the centrality concepts though, so he taught us how betweenness centrality, closeness centrality, and degree centrality would be used to analyze nodes in the data graphs. The centrality established that the higher the centrality value, the more "central" that point is. If you imagine the data graph to be a social network, the node with the highest centrality is the most popular person. After visiting Caltech on Thursday...