Applied Machine Learning

Catalog description

This course introduces students to the fundamental principles and techniques of supervised and unsupervised machine learning. Students will learn how to preprocess and analyze data, design, and train various machine learning models, and evaluate the performance of those models. Through hands-on exercises and projects, students will gain experience with popular machine learning tools, frameworks, and solutions implemented in on- premises and cloud environments. 3 credit hours, didactic MS662 – Health Informatics This course covers the fundamental principles and concepts of health informatics, including the design, development, implementation, and evaluation of health information systems. Students will learn how to use information technology to improve outcomes pertaining to healthcare delivery, patient care, and population health. Course topics include health information standards and interoperability, electronic health records, health data analytics, clinical decision support systems, and health information privacy and security. 3 credit hours, didactic