I taught the following courses (or a condensed version in the case of Machine Learning for a graduate workshop) at Vanderbilt University during the 2017-2018 academic year. Syllabi for each course are available for download via the links below:
(Instructor, Summer 2017, Summer 2013, Summer 2012)
(Teaching Assistant, Spring 2012 with Valeria Sinclair-Chapman)Syllabus
The currents of American political life are defined by the constraints that enduring political institutions apply to channel evolving public opinion. Much of the tumult in American politics we see today can be understood through consideration of the foundations of American government, major political institutions, and mechanisms that link citizens and government. This course will examine how the government of the United States is organized, the rationale behind its organization, and the ways citizens, political actors, and political institutions interact to achieve political goals. This course is appropriate for political science majors who wish to gain a foundation in American politics as well as for non-majors who simply wish to gain a better understanding of American government and processes.
(Instructor, Spring 2018)
(Teaching Assistant, Fall 2012, Fall 2011, Fall 2010, Spring 2009, with Michael Peress)Syllabus
The ability of political science research to shed light on pressing questions in contemporary political life is only as strong as the quality of the research designs used by political scientists. Gaining and sharing insight into the behavior of human beings within the sphere of politics requires a strong understanding of the logic of causal inference underlying empirical research in the social sciences, a capacity to gather useful political data and analyze it in an informed manner, and the ability to share research with others through scientific papers and formal presentations. This course will cover all of these aspects of social science research and leave students with the basic toolkit to ask good questions and design and execute plans to answer those questions. While this course is predominantly focused on quantitative research methods, qualitative methods will be discussed to a lesser degree. This course is appropriate for political science majors who wish to gain a foundation in political science research methods as well as non-majors who simply wish to gain a better understanding of social science research.
(Condensed Graduate Workshop - Instructor Spring 2018)Syllabus
Perception and reasoning by intelligent systems requires the processing of inputs into a reasonable mapping of the world, learning from experience, and management of the uncertainty that pervades the world around us. This course will provide a firm introduction to machine learning and the design of intelligent systems capable of handling these three tasks. The course will engage with machine learning by developing the theoretical and mathematical underpinnings of a variety of algorithms, and apply successful algorithms in several contexts to witness their limitations and strengths. This course is appropriate for graduate students and computer science undergraduates comfortable with programming who have basic training in probability theory and familiarity with linear algebra and calculus.
Probability and Inference; Linear Models (Graduate); Maximum Likelihood Estimation (Graduate) ; Introduction to Python; Text as Data (Graduate)
Introduction to Game Theory; The Presidency ; The Congress; Campaigns and Elections; The Public Presidency (Graduate)
Political Psychology; Political Communication; Personality and Politics (Graduate)