The 2019 spring term ended a week ago and I’ve been procrastinating on how ML4T (and IHI) went. I’ve known all along that writing is DIFFICULT, but recently it seems significantly more so.
Perhaps its because I’ve noticed this blog has been getting a lot more traffic recently. This includes having Prof Thad Starner commenting on my post for his course on Artificial Intelligence. This has increased my own expectations of my writing, making it harder for me to start putting pen to paper.
To tackle this, I looked to the stoicism techniques (i) to decide if something is within my locus of control, and (ii) to internalise my goals. Is it within my control how much traffic my writing receives? No. Is it within my control how much feedback I get on my writing? No.
Instead, what is within my control is writing in a simple and concise to share my views on the classes, so others can learn from them and be better prepared when they take their own classes. This has been the goal from the start—I guess I lost track or forgot about it over time, and got distracted by other metrics.
With that preamble, lets dive into how the ML4T course went.
Why take the course?
My personal interest in data science and machine learning is sequential data, especially on people and behaviour. I believe sequential data will help us understand people better as it includes the time dimension.
In my past roles in human resource and e-commerce, I worked with sequential data to identify the best notifications to send a person. For example, you would suggest a phone case after a person buys a phone, but not a phone after a person buys a phone case. Similarly, in my current role in healthcare, a great way to model a patient’s medical journey and health is via sequential models (e.g., RNNs, GRUs, transformers, etc). I’ve found that this achieves superior results in predicting hospital admissions and/or disease diagnosis with minimal feature engineering.
Thus, when I heard about the ML4t course, I was excited to take it to learn more about sequential modelling—stock market data is full of sequences, especially when technical analysis was concerned. In addition, framing the problem and data from machine and reinforcement learning should provide useful lessons that can be applied in other datasets as well (e.g., healthcare).