Other Programming Language

This course introduces theories and methods used for the analysis of political networks. Political networks describe how political actors - such as participants of the policy process - form and maintain relationships, and the analysis of political networks can help us to understand how these relationships influence political or policy outcomes. Network concepts are increasingly prevalent across a wide range of social science disciplines, and are often used as a tool to study complex phenomenon such as cooperation, diffusion of innovation, and social capital. This course will introduce students to major research questions in the study of networks, as well as their applicability to understand real-world problems in public policy and political science. Students will learn, through hands-on training in R, how to manage network data and perform essential descriptive and inferential analyses on these data.

Fundamentals of formal language theory; syntactic and semantic processing; the place of world knowledge in natural language processing. Graduate-level requirements include a greater number of assignments and a higher level of performance.

Students will learn from experts from projects that have developed widely adopted foundational Cyberinfrastrcutrue resources, followed by hands-on laboratory exercises focused around those resources. Students will use these resources and gain practical experience from laboratory exercises for a final project using a data set and meeting requirements provided by domain scientists. Students will be provided access to computer resources at: UA campus clusters, iPlant Collaborative and at NSF XSEDE. Students will also learn to write a proposal for obtaining future allocation to large scale national resources through XSEDE. Graduate-level requirements include reading a paper related to cyberinfrastructure, present it to the class, and lead a discussion on the paper.

Bayesian modeling and inference is a powerful modern approach to representing the statistics of the world, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to molecular biology to astronomy. Graduate-level requirements include different exams requiring greater depth of understanding of topics, and will be assigned questions based on graduate-student specific assignments topics.