Learning Machine Learning with Aston

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...