Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, bias, variance, consistency, sampling distributions, estimating equations, information, bootstrap methods, ROC curves, shrinkage, large sample theory, prediction optimality versus estimation optimality.

Various topics offered in Global Studies. See department website for full course descriptions. 

This course is focused on an exploration of "self" in relationship to the complexities and structures of the professional organizations in which students work as interns. The course combines organizational behavior concepts and content that emphasizes self and exploration.

The course is focused on an exploring the dynamics of teams and leadership within the complexities and structures of the organizations in which students work in professional practice internships. The course combines organizational behavior with concepts of teams and organizations.

Students apply academic experiences in professional and/or research settings; reflect and critically and constructively analyze experiences from multiple perspectives; and view the work as connecting course content authentic contexts. Students work as professionals with site supervisors and instructors to complete related assignments and relevant background research on the professional and academic resources available.

An Independent Study in Archaeology. Subject to be determined by student and instructor.

Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic dashboards for effective data communication with end-users. Covers theoretical concepts and hands-on implementation to provide a comprehensive understanding of the full design process.

Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine learning methods. Covers definitions, decision support, trust, and ethical considerations, and the latest advances in creating reliable and transparent AI models.

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.

This class provides a general overview of film production in Brazil since 1990. We will screen and discuss a variety of documentary and feature-length fiction films, paying special attention to their formal construction and respective portrayals of Brazilian society, particularly as they unfold in a context increasingly marked by globalization and neoliberalism.