To promote studies using computational methods in social sciences, this course focues on machine learning techniques and their applications in social sociences. By introducing principal ideas in statistical learning, the course will help students to understand the conceptual underpinnings of methods in data mining, how classical concepts of research design in the social sciences can be implemented in new data sources, and how these new data sources might require social scientists to update their thinking on research design. The course focuses more on the usage of existing software packages (mainly in R) than developing the algorithms by the students. Students will be required to work on projects to practice applying the existing software.

CILO-1: Interpret the process of research design and implementation in social sciences. CILO-2: Explain the conceptual underpinnings of methods and algorithms in machine learning, and be able to choose the proper methods for analysis. CILO-3: Identify pros and cons for implementing machine learning methods in social science studies. CILO-4: Apply basic statistical and machine learning models for social science data.