Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference. A course covering statistical techniques such as regression.

Focuses on the application of communication skills and principles in the context of public safety. Students will gain understanding and practice in engaging communities around such challenging issues as inequality and power; interactions in the aftermath of tragedy; officer fear and anger; historical, political, and economic divides; implicit biases and stereotype threat; and the importance of building coalitions across boundaries.

Students explore how insights from various disciplines inform their understanding of healthcare. Guest lectures and informational interviews connect students with healthcare professionals to gain a better understanding of the various health professions and to assess their own career goals. Students develop skills in interdisciplinary research and problem solving, in oral and written communication, and the integration of diverse perspectives.

In this class, students will learn to critically reflect on one's own situation and perspective in relations to one's expanding knowledge of other human experiences, seeking to cultivate a framework for informed reflection on human diversity and social complexity while developing empathy as a foundation for democratic citizenship.

Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, Bootstrap methods, ROC curves, shrinkage, and some large-sample theory, prediction optimality versus estimation optimality.

Explores public safety leadership concepts and essential approaches needed in forging lasting, collaborative relationships with the public they serve. Students will analyze complex social and security issues. While maintaining a mindset of sociocultural awareness and sensitivity, students craft solutions to those public issues by applying advanced knowledge of public safety planning, management, and response. Prereq: Admission to MPS Degree Prog.

Explores principles and applications of data ethics within a broader social framework that prioritizes conversations about policy, regulatory frameworks, accountability, transparency, and governance models. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities.

This is the capstone seminar for students in the Security and Justice track of Global Studies.

For students advanced beyond the level of ENCW 2600. Involves workshop of student work, craft discussions, and relevant reading. May be repeated with different instructor. For instructions on how to apply to this class or more details, please visit our program website at creativewriting.virginia.edu/ugrad.

Caring well for an aging population is among the greatest challenges facing both the United States and the world. Significant gaps persist between the health and social systems that older adults need, and those to which they have access. This course uses a multidisciplinary approach--encompassing history, public health, ethics, the social sciences, and literature--to explore these gaps, their impact, and their meaning.