Scott V. Burger - Introduction to Machine Learning with R: Rigorous Mathematical Analysis

Its always been a dream of mine to write a book. When I was in third or fourth grade, my ideal book to write would have been a talk show hosted by my stuffed-animal collection. I never thought at the time that I would develop the skills to one day be shedding light on the complex world of machine learning. Between then and now, so many things have happened that I need to take a moment to thank some people who have made this book possible in more ways than one: Allison Randal, Amanda Harris, Cristiano Sabiu, Dorothy Duffy, Elayne Britain, Filipe Abdalla, Heather Scherer, Ian Furniss, Kristen Brown, Kristen Larson, Marie Beaugureau, Max Winderbaum, Myrna Fant, Richard Fant, Robert Lippens, Will Wright, and Woody Ciskowski.

There was a time in my undergraduate physics studies that I was excitedto learn what a model was. I remember the scene pretty well. We were ina Stars and Galaxies class, getting ready to learn about atmosphericmodels that could be applied not only to the Earth, but to otherplanets in the solar system as well. I knew enough about climate modelsto know they were complicated, so I braced myself for an onslaught ofmath that would take me weeks to parse. When we finally got tothe meat of the subject, I was kind of let down: I had already dealtwith data models in the past and hadnt even realized!

Because models are a fundamental aspect of machine learning, perhaps itsnot surprising that this story mirrors how I learned to understand thefield of machine learning. During my graduate studies, I was on thefence about going into the financial industry. I had heard that machine learning was being used extensively in that world, and, as a lowly physicsmajor, I felt like I would need to be more of a computational engineerto compete. I came to a similar realization that not only was machinelearning not as scary of a subject as I originally thought, but I hadindeed been using it before. Since before high school, even!

Models are helpful because unlike dashboards, which offer a static pictureof what the data shows currently (or at a particular slice in time),models can go further and help you understand the future. For example,someone who is working on a sales team might only be familiar with reportsthat show a static picture. Maybe their screen is always up to date withwhat the daily sales are. There have been countless dashboards that Iveseen and built that simply say this is how many assets are in rightnow. Or, this is what our key performance indicator is for today. Areport is a static entity that doesnt offer an intuition as to how itevolves over time.