Personally, the main takeaway from this course apart from appreciating and applying the core framework (the one where inputs, prediction, and evaluation are all mentioned) is the role of theory in our predictions. The modern rise of ML and Big Data have surely empowered Professors like Susan Murphy to answer human problems with data-backed solutions that previoulsy would have been the subject of many experiments of doctors or phycologists usually in societies with lax ethics rules regarding human experimentation. Yet, it is telling that some of the most important work occurs when employing a theory that many smoke in order to relive stress, so how can we provide them an alternative to this method of stress-relief. This is something we have known for a while and any smoke and any non-smoker with smoker friends observes this with little to no effort. And indeed she mentions unlike Meade where Earthquake predictions struggle with limited data and limited, Murphy is working with a lot of data but potentially not enough theory, so her focus is interdisciplinary work in order to test and develop theories that can focus the direction of her data projects as well as ground her work in explanable theories. This in fact might demonstrate why we need interdisciplinary work to begin with. We have a lot to learn from other people in regards to theories their fields have developed and tested that may be relevant in our predictions.