This course is an introduction to clinical ethics, which concerns the identification, analysis, and resolution of ethical problems arising in patient care. The course deals with two tiers of ethical problems occurring frequently in the clinical setting: those that are present in every clinical encounter and special problems that arise with particular patients.

Examines how informatics in the healthcare industry improves patient care, health outcomes, efficiency, quality & evidence-based decision making; evaluates challenges and strategies for healthcare managers in implementing health information systems & their costs, benefits, and impacts in healthcare organizations. Analyzes key issues in data management, security, privacy and confidentiality. Prerequisite: Admission to HSM Graduate Certificate

Independent research for undergraduate students enrolled in the BA/BS/MPH 5 year program or other programs that may be related to Public Health or Ethics.

This course serves as the research methods seminar for advanced undergraduate students and graduate students in a variety of regional studies programs, including (but not limited to) European Studies, East Asian Studies, MESALC, and Contemporary Russian Studies. Coverage of specific regions and methods varies from year to year, depending on students' specialist areas of interest.

Introducing the questions, methods, and arguments that organize work in the Environmental Humanities (EH), this graduate seminar is open to MA and PhD students from any discipline, including the sciences and social sciences. There are no prerequisites. ENVH 6000 serves as the required course for the Graduate Certificate in Environmental Humanities, yet is open to graduate students not pursuing the Certificate.

This interdisciplinary course introduces students to advanced research methods for investigating issues in European Studies. Each student will develop a research proposal and paper on a specific disciplinary topic under the supervision of a faculty member in that discipline, with the requirement that the paper include significant insights from at least one other discipline.

Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects. Capstone course is for MSDS students.

Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

This course introduces first-year graduate students in the humanities and social sciences to the knowledge and skills fundamental to success in graduate school. Particular topics vary.

Evolution of language models, from encoding words to simple vectors to training LLMs. Train and build LLM, understand concepts like self- and cross-attention in LLMs and their applications, review research on Tokenizers, Retrieval Augmented Generation (RAG), Prompt Engineering, Fine-tuning LLMs using Low-Rank Adapters (LoRA), Quantization in LLMs, QLoRA, In-context Learning (ICL) and Chain-of-Thought (CoT) reasoning. Using Python libraries.