Applied AI/ML in Biotechnology

Catalog description

Applied AI/ML in Biotechnology explores how data-driven tools are reshaping decision-making across the drug development lifecycle. Rather than focusing on coding or theory alone, this course trains students to become translational thinkers, able to frame meaningful questions, interpret machine learning models, and apply insights to real-world challenges in R&D, clinical trials, commercialization, and manufacturing. This course follows the end-to-end biotech value chain through five modules, where each focus on a critical decision point: How can we responsibly apply machine learning to biological problems? Which targets should we pursue? Hit Discovery & Lead Optimization: Which candidates show the most promise? Which patients should we enroll, and how should we design early trials? How can we forecast demand and ensure scalable, efficient production? Students will build hands-on skills in R, work with real-world datasets, and complete practical labs aligned to each stage.