Devoted to the writing of prose fiction, especially the short story. Student work is discussed in class and individual conferences. Parallel reading in the work of modern novelists and short story writers is required. For advanced students with prior experience in writing fiction. May be repeated with different instructor. For instructions on how to apply to this class, see creativewriting.virginia.edu/ugrad.

For advanced students with prior experience in writing poetry. Student work is discussed in class and in individual conferences. Reading in contemporary poetry is also assigned. May be repeated with different instructor. For instructions on how to apply to this class, see creativewriting.virginia.edu/ugrad.

Reading and research under the direction of a faculty member. Students must obtain approval from a faculty advisor to approve and direct the independent study. Final approval by the Director of Undergraduate Programs is also required.

Research for a thesis of approximately 50 written pages undertaken in the fall semester of the fourth year by archaeology majors who have been accepted into the Interdisciplinary Archaeology Distinguished Majors Program. Prerequisite: acceptance into Archaeology DMP

This course introduces students to a variety of disciplinary approaches to the study of Europe (history, anthropology, sociology, political science, economics, and culture). Stress will be laid on how combining perspectives from different fields of study can help deepen understanding of specific problems of European life. Emphasis on student interpretation of readings and analysis of central issues in Europe's development across time.

Covers foundations and applications of NLP with a focus on the most popular form of unstructured data - text. Convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and GenAI techniques. Focus is on unsupervised methods to explore cognitive patterns in texts, with real-world examples and demonstrations.

Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and safety data; and unstructured data, such as clinical text data; biomedical sensor data; and biomedical image data. Querying with SQL, data visualization with Tableau, and analysis and prediction with Python.

Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter estimation, decision theory, and hypothesis testing. Topics include parametric and nonparametric methods, Bayesian and frequentist paradigms, and applications of uncertainty in real-world problems. 

Genomics Foundations introduces core concepts in modern genomics and human genetics underlying computational biology and public health genomics. The course integrates key biological principles with quantitative reasoning and hands-on use of real genomics data and databases.