There are a number of courses students must successfully complete to receive the MS in AIDD. Please read the descriptions below for more information about these courses.
PLEASE NOTE: It is mandatory for students to select a course schedule (the four-semester or the seven-semester course schedule) at the beginning of the program.
Course Descriptions
Students take all eight courses for a total of 30 credits.
AIDD 601: Introduction to Drug Development (3 credits)
This graduate-level course provides a comprehensive overview of the drug development process, from drug discovery to post-marketing surveillance. Students will explore the fundamental principles and practices of drug development, including regulatory requirements, preclinical and clinical testing, pharmacovigilance, and marketing approval processes.
AIDD 602: AI Methodology I (4 credits)
The applications of Artificial Intelligence and Machine Learning (AI/ML) methodologies are ubiquitous, and the pharmaceutical industry is rapidly adapting to the AI/ML advancements in drug development. This graduate level course will provide an introductory exploration into the methodology and techniques of AI/ML. Students will learn fundamental concepts, methods, and best practices in AI/ML, including problem formulation, data preprocessing, model selection, evaluation, and interpretation.
Students learn and apply supervised learning techniques in this course. Through lectures, hands-on exercises, and real-world case studies, students will gain practical skills to apply AI/ML methodologies to problems relevant to the health care and drug development domains. Students will learn fundamentals of AI/ML programming using the open-source Python programming language.
AIDD 603: AI Methodology II (4 credits)
This graduate level course will teach intermediate to advanced level concepts and methodology of AI/ML. Students will continue to learn advanced concepts, and best practices in AI/ML, including problem formulation, data preprocessing, model selection, evaluation, and interpretation. Students learn and apply unsupervised learning techniques and neural networks. Through lectures, hands-on exercises, and real-world case studies, students will gain practical skills to apply AI/ML methodologies to problems relevant to the health care and drug development domains. The students will learn fundamentals of AI/ML programming using the open-source Python software.
AIDD 604: Drug Development Strategy (4 credits)
This graduate-level course provides a comprehensive overview of the strategic aspects of drug development, focusing on the critical decisions and considerations that drive successful drug development programs. Students will explore the key principles and practices of drug development strategy, including target product profile, regulatory strategy, market access, and lifecycle management.
AIDD 605: Application of AI/ML to Pharmacovigilance (4 credits)
This graduate-level course provides an in-depth exploration of the application of Artificial Intelligence and Machine Learning (AI/ML) techniques to pharmacovigilance, the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other drug-related problems. Students will gain a comprehensive understanding of the role of AI/ML in improving pharmacovigilance processes, including adverse event detection, signal detection, risk management, and regulatory reporting. The course will cover fundamental concepts of AI/ML relevant to pharmacovigilance, such as data preprocessing, feature selection, model development, and evaluation.
AIDD 606: Precision Medicine (4 credits)
In the era of precision medicine, harnessing the power of Artificial Intelligence and Machine Learning (AI/ML) is paramount for making informed and personalized health care decisions. This advanced course delves into the cutting-edge methodologies and computational techniques essential for analyzing complex datasets and optimizing treatment strategies tailored to individual patients. Students will explore theoretical foundations and practical applications of AI/ML in precision medicine, focusing on the integration of diverse data sources including genomics and clinical outcomes. Emphasis will be placed on understanding the mechanistic insights derived from molecular data and integrating them with statistical models to predict patient responses and optimize treatment regimens. Hands-on sessions will provide students with proficiency in utilizing state-of-the-art AI/ML tools and software platforms for data analysis and visualization. Through case studies and real-world examples, students will develop critical thinking skills to address challenges in personalized health care delivery and translate research findings into clinical practice.
AIDD 607: Optimizing Clinical Research (4 credits)
AI-enabled Optimization of Clinical Research delves into the strategic integration of artificial intelligence tools to streamline and enhance various aspects of clinical research, equipping participants with the skills to optimize trial design, data management, and decision-making processes in the evolving landscape of health care research. This cutting-edge program is designed to equip students with the knowledge and skills to harness the power of AI/ML in clinical research settings. Across three dynamic modules, students will explore diverse topics such as predictive modeling for treatment response, leveraging natural history data for rare diseases, and endpoint selection using digital biomarkers in decentralized clinical trials. Through engaging lectures, real-world applications, and hands-on assignments, students will gain a deep understanding of how AI/ML techniques can revolutionize clinical research, leading to more efficient trial design, precise patient selection, and improved health care outcomes.
PHA 758: Special Topics (3 credits)
Special Topics will cover contemporary subjects not addressed in other courses, featuring insights from industry and government leaders through invited lectures, providing a unique and comprehensive perspective on emerging themes in the field of AI and drug development.