Yonsei University

This course examines some major topics on European Modernism. It focuses on the philosophies of the influential modern and contemporary European thinkers such as Ludwig Wittgenstein, Martin Heidegger, Friedrich Nietzsche, etc. Topics include Language, Poetry, European Nihilism, Power, Modernity, Value, etc.

This course is a thematic introduction to the major artistic and cultural trends of East Asia, with a focus on the history of Chinese, Korean, and Japanese art. We study major developments and issues in the art of each culture, discussing mutual influences and cross-cultural artistic flows, as well as the many cultural and artistic differences between cultures in the region. Major monuments of East Asian art will serve as our primary evidence. We focus on how to look at works of art and architecture in an art historically-informed way, how to articulate what our visual responses might mean, and how to begin answering some of the questions our observations of the objects may raise. Our goal is to enable you to better appreciate, analyze, evaluate, and interpret works of art, both those that seem familiar at first glance and those that do not. In addition to becoming familiar with major works of art in weekly slide lectures, you are expected to develop, through weekly readings and discussion, an understanding of the various approaches major scholars in the field of art history and East Asian studies have developed to examine them. You are expected to evaluate and try out some of these methods in your own research, written work and class discussion. The course is divided into three discrete sections that focus respectively on China, Korean, and Japan. Although these three regions engaged in extensive cultural interchanges during the period of time covered by this course, each also developed its own artistic styles and forms. Discussions of these cross-cultural interactions are constant subtheme, especially as our shared understanding grows over the course. Whether the aims of their creators were philosophical, spiritual, political, social, economic, or purely aesthetic, we seek to better understand them, as well as the context in which they were acquired and cherished, the uses to which these monuments may have been put, and the grounds for both their original and subsequent appreciation. Thus, the goals of this course include developing visual and historical tools you can use outside the confines of this class to explore art and visual culture.

The course critically evaluates rock music's musical content and contemporary cultural and social roles; ideally, the course serves to develop your general intellectual capacities of the music industry from the 1950s to 1960s (the so-called "rock and roll" era, arguably the most turbulent yet important period in popular music history). It's NOT a music course, per se, but we are listening to a lot of music as we consider the effects of recorded sound on popular culture. Thus, this is the quintessential "media and culture" course. We study the origin and growth of the recording industry and music business, consider the impact new technology had (and continues to have) on the development of popular music and examine the mutual influence between rock music and other media (film, television, radio, etc.). Following a loose chronology, we begin with an introduction to listening and some musical fundamentals, gradually developing a vocabulary with which to discuss and experience selected works from the history of rock. We trace the evolution of specific musical styles and investigate issues related to culture, performance, technology, and reception. Reading assignments introduce the distinct musical styles, performers, and works that comprise each genre and a certain time period. They also cover the relationship of rock music to American and global popular culture, historical representation, and authenticity.

This course provides a basis for handling and analyzing business data. This course also gives a chance to learn R, which is becoming a default analytics platform. This course presents students with real datasets and gives opportunities of ‘learning by doing’ through hands-on experience. Specifically, we study basic concepts in business analytics, and techniques and skills related to data exploration, data utilities, conducting statistical tests, data mining, and causal inference modeling. Topics include web data crawling and analysis, big data, regression models, panel model, classification model, causality model, instrumental variable model, and matching model.

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.