Machine Learning in the Life Sciences

Catalog description

Machine Learning sits at the intersection of statistics, probability, computer science, and information theory. Its primary goal is to develop systems that can learn from experience to solve problems across various domains. However, this class focuses on applications in life sciences and some intersections with healthcare and business. This course is designed to introduce students to fundamental concepts in machine learning, enabling them to understand and assess literature effectively. Topics covered include regression, classification, clustering, dimensionality reduction, model evaluation and selection, and feature engineering. This class also offers hands-on experience with real-world data from diverse sources that students use to work on projects. Since this is an introductory class, it is self-contained, but basic knowledge of Python, statistics and probability, and linear algebra would be helpful.