Machine Learning is perhaps the hottest key word in the tech world nowadays. I have to admit that it was due to this popularity, along with my interest in both computer science and mathematics, that I developed interest in this field. Nevertheless, I had a strong feeling that research in this field was something I had to experience. This summer, I expected to learn about all the cool machine learning tools and apply them to real world problems. I was mistaken. Research in machine learning was more theoretical than I had imagined. It was more about statistics and optimization than coding, and I used synthetic data more than I used actual real world data. This was a bit disappointing at first, but as I immersed myself deeper into the research, I found myself really enjoying the process of problem solving. The project I worked in was about learning the nested logit tree structure of choice data. The first part was recreating results of an algorithm in a research paper by Google on this topic. Then I implemented a new algorithm that may perform better than the algorithm in the paper. Also, I implemented code that created plots of maximum likelihood estimator computations that show the performance of the algorithms. Creating synthetic data, making the tree datastructure in Matlab, implementing an algorithm from a reference paper, and conducting experiments in the Bayes cluster (distributed computing system hosted by Penn SEAS) were all new problems that I have never encountered before. It was challenging yet reasonably doable and I was excited each time I moved a step forward. I believe that Prof. Agarwal gave me a suitable project considering my background. Also, it was a great experience to work alongside a postdoc (Nick). All in all, I am excited about continuing to work in another research, perhaps a longer project with more interesting things to learn about machine learning.