Korea, South

This course introduces the general principles and mechanisms of drug actions including those that influence the absorption, distribution, biotransformation (metabolism), and excretion of drugs. Course topics include: clinical applications, adverse effects, drug toxicity, and structure-activity relationship. The course focuses on the pharmacology of the autonomic nervous system, central nervous system, and autacoids.

This course is a broad introduction to computer vision for data science. Topics include various low-level image processing methods and high-level vision tasks like image classification and object detection, with modern approaches based on deep learning. Student learn computer vision algorithms and implement them in python. Students should be proficient in python programming with numpy for assignments. Other python libraries, such as opencv (cv2), scipy, and matplotlib will be used, but students do not have to be proficient with them.

The goal of this course is in introducing popular skills for analyzing economic data. We attempt to achieve this goal by getting familiar with the well-known econometric analyses and linking this to the knowledge on the numerical outputs generated by standard statistical packages. In attaining this goal, our interests will be focused more on cross-sectional data and their slight extensions. There are two reasons for this focus. First, analysis of cross-sectional data is a building bloc for the analysis of many other data sets. Second, the analysis of cross-sectional data is easier than analyzing other data sets as they do not involve too much complication that comes from the variation assumptions. Eventually, by these, studying cross-sectional data becomes a good starting point for achieving the specified objectives, even though their applicability is not so limited. After completing this course, students are expected to be able to conduct the following: Understanding the implicit assumptions behind economic data analysis; Interpreting the numerical outputs generated by standard statistical packages.

Prerequisite: Mathematics for economics and statistics; Recommended: Mathematical statistics.

This course examines the literature of wisdom, both ancient and modern, and looks at how reading literature can deepen, enrich, and improve one's life in modern society.

The course discusses the significance of interpersonal relationships and introduces several theories which systematically explain the psychological factors in human relationships. The course focuses on the relationships between family members; friends; colleagues, and persons of the opposite sex.

This course aims to provide basic mathematical concepts that have been widely used in economics. Topics include feasibility (Farkas Lemma), convex sets, linear programming, and non-linear programming. Economic applications are discussed throughout the course in order to illustrate how mathematics is used in economic theory.

Prerequisite: Mathematics for Economics I, Microeconomics

This course takes an anthropological approach to the questions of why and how media matter. More specifically, the anthropology of media explores media as cultural practices and investigates how people navigate and create media worlds. It draws ethnographic attention to the socio-cultural contexts of media and poses questions about how media fit into societies at large.

This course introduces the major concerns, methods and ongoing debates of this new and vibrant field. Drawing on case studies from around the world (but mostly from East Asia), we explore how media practices are defined not only by available technologies but also by societal infrastructures and cultural needs; how the actual circulation of media escapes the desires and intentions of media producers; how media audiences appropriate mass media to their own ends; how old and new media are implicated in social and political change; how media shape national, ethnic and gender identities; and what challenges these complexities present to researchers of media.