> I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. Application of Computational Stats/ML Mode

> I used to think an MLE was a solid engineer who also had a strong quantitative and numerical computing background. Application of Computational Stats/ML Models are not all that hard to aquire but essential. I think we need a fundamental rethink of how applied stats/ML is taught to engineers to make them effective. Here are a few things I can think of: 1. Getting a solid understanding of actually coming up with a simple enough model to do the job 2. Do Power Analysis to figure out how many samples we need. Creating datasets with Hard Negatives and overcoming sampling bias. 3. Using things like Multiple Regression to do EDA. i.e. using models as a tool vs the end goal to understand a problem space.