Nov 29 - Dec 19 Journal

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 when our survey data indicates a skew or preference, we would also add a bias constant in the equation.


With this introduction, the group and I decided that our winter break project would be trying to derive a linear predictor equation from our survey data. We would all have our own unknown feature and would use the remaining questions to guess. According to Dr. Hassibi, the more evenly distributed questions would be harder to predict due to their more polarized nature. I started working on the data in class with the csv file creation and looking around tutorials. The tutorials differ though, so I will probably have to further look into them later.


Paper Airplanes:
During this time, we also wrote our Discussion section for our paper airplane research. We are also turning in our final reports tomorrow! I found it exciting to finally bring together the many different parts of our research. My final report was 17 pages and I was quite proud of how much I produced and learned from this experience. The Discussion section was also a great opportunity for me to reflect on the whole paper airplane research.


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