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...

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...

Clustering : These two weeks, I researched the k-means clustering algorithm after the group split amongst ourselves different clustering algorithms. I already knew the basics of k-means, that data points would be clustered depending on the centroids that would be shifted until all the data stayed in the same clusters. One of the key issues with the k-means algorithm, however, is finding exactly how many clusters the algorithm should create, because the user must input the "k" value, the number of cluster. I found that the "Elbow Method" (pictured below) was the most common way to determine the "k" value. The Elbow Method uses the Least Squares Method to calculate how many clusters minimizes the distance between centroids and the cluster data points. I also learned of how the k-means algorithm can be applied. This clustering can be used to cluster customer purchases, personality test respondents, or even typical Youtube recommendations. During t...

Decision Trees: This week, I continued looking into decision trees with my concept map being conditionals (if/elif/else statements) and also working a bit on the Titanic survivability tutorial. Before the new deadline for the concept map was set, I decided to complete my concept map entry due to other tests later in the week and compared conditionals to ordering at In-n-Out. I explained how conditionals are used by the decision tree to create the branches and categorize the data. I also learned about the entropy (randomness) of the decision tree from Will today with the group. I thought the discussion with Will and the group was very informative but had reservations about the extent to which each person should research individually. My concern was how much individual research is too much so that our skillsets do not become so different that it impedes discussion. A possible area of further research is the random forest trees area within decision trees. Learning Decision Trees Ent...

This week the machine learning group and I explored Decision Trees and I also continued work on the paper airplanes project, meeting Dr. Hassibi to discuss our research plan at Caltech as well. Decision Trees: After completing the Concept Map on Linear Regression (my subconcept was the derivative), the group and I looked into Decision Trees through the David Fumo Medium articles. The first article revolved around the iris dataset and using decision trees to classify an iris into one of three species. Having most of my coding experience either just with the Runestone Textbook or Codeacademy's platform, I was initially very confused as to what to do logistically (how to work PyCharm, pip install, etc). Observing and asking group mates though, I soon began to feel accustomed to the general process and realized that even if I didn't have as strong a foundation, I was here because my personality enabled me to learn, catch up, and excel. After the iris decision tree, we start...