Artificial Intelligence for Biomedical Research

Catalog description

This course provides students with a thorough understanding of AI methods and their use in biomedical research. The curriculum is designed to make students proficient in using the AI and machine learning tools that are essential for modern biomedical investigations. Using problem-based and active learning strategies, the course focuses on a diverse range of AI topics, with a project-based assessment that includes a presentation and written report. This course is especially relevant for students with an interest in bioinformatics and systems biology. It equips them with the foundational skills needed to work with complex, high-dimensional biological data and to apply AI techniques to uncover new insights into biological processes. Furthermore, the course prepares students for advanced subjects in bioinformatics, such as data integration, translational methods, and specialized programming. Upon completing the course, students will be able to articulate core machine learning concepts, including supervised and unsupervised learning, and apply them to problems in bioinformatics and systems biology. They will gain the ability to critically assess the performance of machine learning models using key metrics like accuracy, precision, recall, and the F1 score. Students will also learn to implement machine learning algorithms to solve real-world medical problems, which involves selecting the right algorithms and preprocessing data. The course will also empower students to identify specific challenges in biomedical research where AI can be beneficial and to design AI-powered solutions while considering ethics, data privacy, and the impact on patients. Ultimately, graduates will have the theoretical knowledge and practical experience to collaborate effectively in interdisciplinary teams and engage with AI experts in both academic and industrial environments. Prereq: ( SYBB 402 and SYBB 411 ) or SYBB 412 .