Simon Hoellerbauer, Post Doctoral Fellow in Data Science and Society, has secured a project award from the Liberal Arts Collaborative for Digital Innovation (LACOL) in support of his class titled “Applied Machine Learning” for Summer 2023. Also involved were Monika Hu, Associate Professor of Mathematics and Statistics, Elizabeth Evans (Haverford College), Natalia Toporikova (Washington and Lee University), and Laurie Heyer (Davidson College). Co-taught by LACOL faculty/instructors, this course is designed for students majoring in STEM or Social Science fields outside of Computer Science or Statistics, and will be offered to students from across LACOL institutions.

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 students how to investigate data using machine learning models. It will teach students how to extract and identify useful features that best represent their data. Students 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.

Students will also explore some of the inductive biases of ML methods (i.e. what assumptions about the data and the world are baked into the structure of a given algorithm) and implications for the types of problems the algorithm is appropriate for. Students will examine the potential for misuse of ML - sometimes unintended, sometimes malicious - through concrete examples (drawing on sources such as Atlas of AI) and discuss the value judgments, critical thinking, and societal impacts that must be considered whenever ML is applied in the real world.

Finally, with a team of classmates, students will develop their own machine learning model and apply it to understand real-life data.