Disclaimer: it is quite hard to make important topics of machine learning fit on a one-semester schedule. We aim to make the course reasonably digestible in an introductory manner. We try to focus on a modularity view by introducing important variables to digest machine learning into chunks regarding data/ representation / loss-functions / optimizations / model characteristics. That said, our goals here are to highlight the most foundational design choices in machine learning about algorithm designs, workflows, what to learn and how to learn it, and to expose the trade-offs in those choices. We think this teaching style provides students with context concerning those choices and helps them build a much deeper understanding.