Machine Learning and AI

In the Spring semester of my sophomore year at Boston College I took an Information Systems course with Prof. Ransbotham called Analytics and Business Intelligence. At the time, I was apprehensive about my studies. I didn't have a major, and my creative pursuits felt worthless as a kid at a university driven by its business school– one of the best in the nation for undergrads. I was the odd one out in a course focused on the managerial implications of data science full of MBAs and business students. And while I could care less about the case studies, I became fascinated with the math, engineering and creative possibilities of the tech being discussed.

We used Tableau, R and Python to build models for our final project in which we anayzed a subset of the Yelp Academic dataset. I grew close with Prof. Ransbotham while working on the project, and at the end of the semester, he strongly recomended that I study computer science (to which I obliged-- my GPA never recovered).

As my understanding of computers and how to program them was reaching an 'over-the-hump' feeling, I was only a few months from graduating. As a capstone of my CS education, I decided to take Machine Learning (CSCI 3345 Fall '16) with Prof. Alvarez where I would be forced to leave my comfort zone in math and CS. That experience took my understanding of the theory, methods and applications of artifical intelligence to a whole new level. The cummulation of my coursework here yielded a project that took a look at the effectivness of Recurrent Neural Networks for text generation compared to a Markov Model. The textbook is among the favorites in my library.