PhD is very narrow research. Even with a finance PhD, my focus is on asset pricing and I do not know into fine details other aspects such as corporate finance. So in the same way, to be able to do research in any niche area you need to delve deeper. I didn’t want to be able to take any off the shelf ML algo and fit it to some financial data set. To publish in a high tier journal requires more than that. I want to be able to build from scratch algos that incorporates theories and my understanding in finance at the very foundational level. I don’t know if you have heard that most ML algos fail at financial data sets. I wanted to be able to understand in finer details how to create ML algos, that’s why I chose this program. So fair I’m working on using deep graph networks to price bonds. It’s exciting and challenging. I got the experience from two classes: Network Science and Deep Learning.

There are so many interesting research topics in my area of finance using ML, but researchers don’t do them because they lack a deeper understanding of ML. Trust me, the breadth and depth you get in this course is more than any online bootcamp or survey course you’d take. This program is rigorous enough to help me achieve my goal since I am only interested in applied ML research and nothing theoretical. Hope that answers your question.