Frequentist inference

This graduate course will train you in the methods, conceptual approaches, and theories of computational social science. It is set up to welcome people from many different backgrounds, particularly those with or without prior exposure to programming, statistics, or the social sciences. You will learn how to design research projects and answer research questions motivated and situated in social sciences discourses and theories. The course will survey canonical and cutting-edge methods and techniques. This includes methodological approaches in Big Data analysis, data visualization, social network analysis, agent-based modeling, and natural language processing. You will learn to identify and develop variables, mechanisms, and theoretical framing grounded and motivated within the social sciences. We will also address the growing ethical challenges and considerations associated with computational social science methods and approaches.

Course is a graduate-level seminar in Advanced Research Communication Methods. Students will read primary research in Communication relating to Research Methods and learn the key theoretical perspectives in the area. They will become familiar with current areas of interest in the topic area and future directions. Course will involve lecture, discussion, and the production of graduate level coursework. Specific content areas will vary by semester and instructor.

Course is a graduate-level seminar in Advanced Research Communication Methods. Students will read primary research in Communication relating to Research Methods and learn the key theoretical perspectives in the area. They will become familiar with current areas of interest in the topic area and future directions. Course will involve lecture, discussion, and the production of graduate level coursework. Specific content areas will vary by semester and instructor.

Issues in measurement and sampling in laboratory and field research in communication.

This course introduces the key concepts underlying statistical natural language processing. Students will learn a variety of techniques for the computational modeling of natural language, including: n-gram models, smoothing, Hidden Markov models, Bayesian Inference, Expectation Maximization, Viterbi, Inside-Outside Algorithm for Probabilistic Context-Free Grammars, and higher-order language models. Graduate-level requirements include assignments of greater scope than undergraduate assignments. In addition to being more in-depth, graduate assignments are typically longer and additional readings are required.

This intermediate-level course is a continuation of LING 529 and covers a combination of theoretical and applied topics such as (but not limited to) unsupervised learning (clustering), decision trees, and the basics of information retrieval.

History and structure of the Arabic language in its various forms. Graduate-level requirements include a research paper on any phonological, morphological, or syntactic structure of any variety of Arabic.

The development and exchange of scholarly information, usually in a small group setting. The scope of work shall consist of research by course registrants, with the exchange of the results of such research through discussion, reports, and/or papers.

Machine learning describes the development of algorithms which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself. This course will introduce the fundamentals of machine learning, will describe how to implement several practical methods for pattern recognition, feature selection, clustering, and decision making for reward maximization, and will provide a foundation for the development of new machine learning algorithms.

Quantitative methods in political science and policy research are changing rapidly. The rise of the internet has brought in new sources of text, network, geographical, image, video, and other data. Meanwhile, computing storage and processing capabilities continue to expand, while data and code sharing norms have made it so that anyone with a computer and internet connection can have access to a growing set of tools and methods for modeling and interpreting patterns. This course focuses on the extraordinary work that is emerging in politics and policy as a result of these recent advances, with a broad set of applications ranging from health and defense to environmental and agricultural policy. The course highlights current trends, challenges, and new directions for political and policy researchers in academia, government, and the private sector, focusing on how these new data sources and methodologies are being used to solve problems in social science and public policy.