DS 200 - Applied Machine Learning

Credits: 3

Prerequisite: instructor consent. A pilot online course taught by the Liberal Arts Collaborative (LACOL) consortium, including W&L faculty. Machine Learning is an important modern approach to data-driven decision-making. It brings together computer science, mathematics, and statistics to extract new information from data. It’s commonly used in many STEM and social science disciplines and established as a useful tool to identify new trends and predictions. This class will probe further into the question “what is machine learning?” and teach you how to investigate data using machine learning models. It will teach you how to extract and identify useful features that best represent your data. You will learn a few of the most important machine learning algorithms (e.g. logistic regression, k-nearest neighbors, support vector machines, and random forests) and learn how to evaluate their performance. Finally, with a team of your classmates, you will develop your own machine learning model and apply it to understand real-life data. This course is taught fully online through both real-time (synchronous) and asynchronous delivery with faculty of Bryn Mawr College, Davidson College, Vassar College, Swarthmore College, Washington and Lee University, and Williams College. This course is designed for students majoring in STEM or Social Science fields outside of Computer Science or Statistics. Basic proficiency in R is required through prior coursework such as an Introduction to Data Science class. Student should be familiar with the concept of linear models, data frames, and basic coding.