Course Schedule and Catalog

Department: Computer Science

Meeting: 1:00PM - 2:15PM TR (8-JAN-2024 - 19-APR-2024) 

Part of Term: Full Term

Grade Mode: Standard Letter

Course Type: Seminar

Language of Instruction: Taught in English

Method of Instruction: Face to Face

Credit Hours: 3

 

Section Max Enrollment: 50

Section Enrolled: 9

Enrollment data as of: 20-MAY-2024 2:07PM

 

 

Final Exam: GR Course-Dept Schedules Exam

 

Description: Hospitals continuously collect vast and ever increasing volumes of medical data. This data provided unique opportunities that could improve human health, detect diseases in early stages, and develop personalized treatments. This creates a rapidly changing field of computational medicine with a plethora of algorithms and machine learning methods as well as new and exciting research challenges. In this class we aim to study some of the most advanced methods for computational medicine and look at these challenges from a machine learning and algorithms perspective. We will focus on novel and unique challenges of computational medicine such as privacy, fairness, efficiency, explainability, generalization, learning from multiple modalities, federated learning and analytics, continual learning and so on. The course will specifically focus on methods with provable guarantees such as differential privacy, convergence analysis, algorithmic fairness and so on. Recommended Prerequisite(s): COMP 582