Numerical

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.

When people use the internet, they leave behind traces of their political behaviors and social interactions across space and time. While these digital traces are typically created and collected by businesses and governments for their own internal purposes, they are often available to researchers either incidentally or explicitly on behalf of these organizations. Digital trace data have the potential to broaden the scope and scale of political and social research, but require knowledge of computational tools and methods that are typically not taught to social scientists. Moreover, digital trace data present new legal and ethical considerations, since they often contain sensitive, individual-level information. Digital trace data also present conceptual challenges, some which are not new to political and social scientists, like representativeness, and others that are unique to the web, including algorithmic confounding.

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.

Contributions of the ethnography of communication, language variation studies, and conversation/discourse analysis to the interdisciplinary development of sociolinguistics.

This course provides a hands-on project-based approach to particular problems and issues in computational linguistics.

Topics include speech synthesis, speech recognition, and other speech technologies. This course gives students background for a career in the speech technology industry. Graduate students will do extra readings, extra assignments, and have an extra presentation. Their final project must constitute original work in a speech technology.

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.

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.