An AI-generated image of a brain integrated with computing hardware

Instructor: R. Jordan Crouser

Course Meets: TTh 1:20PM - 2:35PM EST

Location: Young Basement

Jordan’s Office Hours: Mondays 8:30-10AM EST, Fridays 11AM-12PM EST in Bass 105

TA Hours: Sundays 1-3pm EST, Sundays through Thursdays 7-9pm EST in Ford Hall

Course Description:

The field of machine learning (ML) encompasses a variety of computational tools for modeling and understanding complex data. In this introductory course, we will explore many of the most popular of these tools, such as sparse regression, classification trees, boosting and support vector machines. In addition to unpacking the mathematics underlying the computational methods, students will also gain hands-on experience in applying these techniques to real datasets using R and/or python.

Learning Goals:

By the end of this course students will be able to: - Explain the mathematical principles behind classical approaches to ML - Implement ML algorithms in R and/or python - Measure the efficiency and accuracy of various ML algorithms - Reason about the appropriateness of specific ML models in real-world contexts - Use ML to model and reason about real-world datasets