Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.

Understand Deep Learning covering neural networks, activation functions, and optimization algorithms. Gain experience with TensorFlow and PyTorch, mastering key techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Explore transfer learning, reinforcement learning, and natural language processing (NLP), along with industry applications and ethical considerations.

What are the strengths and weaknesses of the major policy-making institutions, and how does the current system of American governance compare with that of other advanced societies? This class will examine the key institutional and political actors in policymaking; focusing on the increasing fole of non-governmental institutions in problem solving.

Explores new models of database design: graph, vector, and ledger. These have become required infrastructure in service of social media (graph databases), Large Language Models (vector databases), and cryptocurrency (ledger databases). Will learn their basic operations with an eye toward other purposes as well as the key advantages and drawbacks of these data models. Center on student projects built using one of these databases.

Introduces the development of Brazilian culture from 1500 to the present. This course is taught in English and does not fulfill the language requirement.

Introduces complex interplay between technology, regulation, and data science and exposes regulatory realities confronting the field. Read and parse regulatory texts. Navigate the international technology regulatory landscape, identify key actors, and appreciate how rules governing different kinds of data, platforms, copyright and intellectual property, and digital services and markets shape data science and AI/ML development practices.

This two-part program will pair students in Rwanda studying at the University of Global Health Equity with UVA students to investigate global health topics relevant to both countries. In groups of four, students work over the course of the year on a research topic of their choice.  eGlobal provides a faculty-supported platform for UVA students to engage in meaningful long-term engagement with international peers.

This two semester course aims to prepare students for effective and culturally appropriate engagement in Global Health activities by providing a background in Global Health theory, key issues, and culturally appropriate practice. Discussion 1-2 landmark articles and case studies focused on Global Health, using the Partners in Health Engage Curriculum supplemented with other relevant articles authored by global partners and UVA faculty.

Various topics offered in Global Studies. See department website for full course description and offerings.

Understanding how location, proximity, and connectivity define global challenges like environmental protection, managing migration, and fostering sustainable economic development is essential for the design of effective solutions. In this course, students will build that understanding through spatial analysis using geographic information systems (GIS) technology and gain the ability to map (literally) pathways to future improvement.