Another semester, another check in. While I didn’t take a class as fun as Statistical Learning, I was able to continue working with Professor LuValle through an independent study. I also received the Statistical Learning book as a gift for Christmas.
Independent Study Statistics and Bayesian Data Analysis were the highlights of the semester, Introduction to Stochastic Processes and Mathematical Theory of Statistics were the secondary highlights, and Exercise & Relaxation and Introduction to Music Theory were my credit filling electives.
Independent Study (Private Repository)
The repository is still a work in progress as I am continuing my research under Professor LuValle next semester. This semester my study partner, Kavi C., and I rewrote LuValle’s code into something more streamlined in hopes of learning the key concepts of his passion code from over the years. There is a writeup on the repository of a quick background, but this next semester I will delve into explaining the project more, specifically our study topic of variable selection of a multiview embedding process. We hope to write and publish a paper!
Bayesian-Data-Analysis Repository
Introduction to Bayesian Data Analysis was another favorite class from this semester. Bayesian statistics is a branch of statistics that seems strongest when trying to estimate population parameters given samples. I don’t think it recreates the wheel as much as Bayesians think, but it does provide more tools that are ‘honest’ with the data. I say ‘honest’ because part of the Bayesian framework is admitting uncertainty in our results, because our results are part of a process of sampling, that represents, but is not the population. Anyways, we got to do a regression project to finish the semester and I worked with my own hiking data. It’s a cool project, but incomplete as I found out by the end of it I could improve it in many aspects, mostly regarding the data I selected to use.
Introduction to Stochastic Processes
This class seems a continuation of Mathematical Theory of Probability, finding ways to predict random variables. We went over Markov Chains, Poisson processes, and Brownian motion, among other topics scrambled between. I love Markov Chains. I enjoyed this class, as I thought it would be the most difficult, but ended up easier than imagined.
Mathematical Theory of Statistics
This was probably the most difficult of the classes, only because I didn’t fully understand what this class was testing until our final. Class was mostly problems as examples to theory, but the tests and homework were problems dealing with theory. And it wasn’t that I had a problem with the theory, but rather which theory was important for the class. There was a lot covered, but only some seemed important to our professor, and I didn’t understand why until our final was given, which was fun and when it ‘clicked’. I finished strong, and will be a teaching assistant grader for the two sections taught next semester.