Independent study to be arranged by student in consultation with professor.

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

Writing of a thesis of approximately 50 written pages undertaken in the spring semester of the fourth year by archaeology majors who have been accepted into the Interdisciplinary Archaeology Distinguished Majors Program.Prerequisite: acceptence into DMP program

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.

Train your own LLM for a custom task. Learn about the LLM lifecycle from architecture, to pre-training, to supervised finetuning, to deployment, to model editing/updating, including discussing LLM limitations. End up with your own trained LLM, a HuggingFace model card you can show off in technical interviews, and a plan for how to stay up to date with this fast-moving field.

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.

This course looks into the past, present, and future of technologies that impact labor, with an eye to empowering students with knowledge about the social, economic, and political dimensions of the tools they use both inside and outside of work. The course covers labor history, whistleblowers, and hidden histories of common technologies that reorient common assumptions about what technologies can do, and what they have done in the past.

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.