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At UW Madison, I am currently the primary instructor for ISyE 521: Machine Learning in Action for Industrial Engineers.
ISyE 521 is a class for both undergraduate and graduate students, and this semester the current enrollement is 55 students, with approximately 50% each of undergraduate and masters students and a small handful of Ph.D. students. The primary content of the course concerns how to be a successful applied practitioner of machine learning, focusing on a critical awareness about how knowledge of machine learning methods and personal expertise in an application domain in question inform and guide each other to be as successful as possible in knowing how to ask and answer the most important and insightful research questions.
Machine learning methods cover linear and logistic regression with regularization, k-means and agglomerative clustering, KNN, CART, Random Forests, Ensemble Methods, Support Vector Machines, and a brief introduction to neural networks and deep learning. Each modelling method is taught in conjunction with a history of its use in historically meaningful research projects, to help illustrate how familiariy with the domain in question, combined with exploratory data and post-hoc analyses, helps to guide modelling choices and produce novel insights into the domain.
The course consists of two 75-minute lectures per week, and under mine and the TA’s guidance, students pursue final projects where they apply machine learning to reach meaningful insights on domains and datasets of their own choice, and communicate their work and findings in professional-style reports and presentations. In past years of this course (when I was the course’s TA), these projects have led to journal publications, startup businesses, and more, so I’m excited to see the outcomes of this semester’s projects!
Beyond primary instruction of courses, I have previously TA’d: