Applied Machine Learning

Catalog description

This course provides a hands-on introduction to Machine Learning (ML), covering fundamental concepts, approaches, and applications. Students will explore some foundational ideas but focus largely on practical implementations of ML techniques in real-world settings, including text and video analysis. The course will be taught in Python and make use of real-world datasets. Topics include model building, model evaluation, supervised classification, large language modeling, unsupervised methods, ethical considerations in ML, and the intersection of machine learning and society. Throughout the course, students will engage in programming assignments that reinforce course concepts, as well as taking exams.