Machine Learning

Catalog description

This course is designed to equip students with an understanding of the most commonly used machine learning methods. The course is structured to provide a blend of theoretical knowledge and practical applications. The curriculum includes a series of hybrid lectures, recorded videos, lab sessions, and presentations, with a focus on various aspects of machine learning such as clustering, dimension reduction, regression, classification, tree-based methods, support vector machines, neural networks, and reinforcement learning. The course focuses on the application of machine learning in healthcare, with lectures dedicated to its use in medical imaging, text data, and omics data. Students will also have the opportunity to explore large language models. The course encourages active learning, with students required to present several times on their chosen class project during the course.