Yonsei University

This course explores the evolving media landscape of 21st century Korea, challenging students to critically analyze and engage with current media forms and trends. We begin with fundamental questions: What is media? What is the current media environment? And what should the ideal media environment look like? These inquiries frame our exploration, which addresses the multidimensional nature of media, the transition from the mass media era to the post-mass media era, and the relevant issues of relevance that arise in contemporary media contexts in Korea. This course redefines "media" in the 21st century by considering it as a pervasive environment rather than just tools or platforms. The course explores the five dimensions of media-tools, content, institutions, people, and spaces-and traces the evolution from the era of centralized mass media to today's fragmented, participatory media environment. This shift has created both opportunities and challenges, particularly around the concept of "relevance" in media. The course examines how the mass media era created a "crisis of relevance" by delivering standardized content that was often disconnected from individual lives. In the post-mass media era, this has morphed into a "super-crisis of relevance," where personal and local stories gain visibility but risk commodification and distortion by global
platforms. Students gain insight into the ethical concerns of the current media environment and strategies for thoughtfully navigating it. By reshaping how we conceptualize the role of media, this course empowers students to critically assess the impact of the media environment on our collective and personal realities. 

This course introduces students to the understanding of space and natural philosophy, aimed at cultivating scientific and interdisciplinary literacy for Yonsei students who will become future leaders of society. It covers the characteristics of science, an overview of the universe, stellar evolution and the origin of humans, and the relationship between science, technology, and the fate of nations. The course also explores galaxy formation and evolution, Big Bang cosmology and natural philosophy, dark matter and dark energy, and the possibility of extraterrestrial intelligence in an accessible way. In addition, it introduces the history and future prospects of astronomical observation.

This course covers basic knowledge of statistics that is essential for exploring communication phenomena empirically and scientifically. Students develop practical statistical analysis skills. Please be aware: this course assumes that students are familiar with communication theory in general and understand social science research methods at a basic level. 

Based on the basic understanding of social science research methods, students will cultivate theoretical knowledge of basic statistical techniques and conduct practical analysis training using R.  

Topics include Communication Phenomena, Theory and Research Methods, and the Theory of Statistics; Basics of statistics; Probability and Probability Distribution; Principles of Statistical Reasoning: Estimation, Hypothesis Testing, Methods of statistical inference; Analysis and Inference of Discrete Data; Regression; Regression Analysis; Fundamentals of ANOVA.  

Prerequisite: Introduction to Mass Communication

This course examines four English Renaissance revenge tragedies, along with screen and stage adaptations. Through close readings and discussions, we situate each play in dialogue with questions of gender, law, and history. We also trace how the genre of revenge tragedy evolved across the period. The plays to be discussed are: Thomas Kyd’s The Spanish Tragedy, William Shakespeare’s Titus Andronicus and Hamlet, and The Revenger’s Tragedy.

This is an introductory course on algebraic geometry. We cover affine/projective varieties, Zariski topology, Hilbert's Nullstellensatz, regular morphisms, Zariski tangent spaces, etc. Topics include Polynomial Rings, Varieties and Ideals, Irreducibility of Affine Varieties, Coordinate Rings, Polynomial Maps, Proof of the Nullstellensatz, Dimension of Affine Varieties, Tangent Spaces and Smoothness, Projective Varieties, Maps of Projective Varieties, Quasiprojective Varieties, and Further Quasiprojective Topics.

Prerequisites: Modern algebra (1), (2), Topology (1)

This course provides an overview of machine learning, a core technology of artificial intelligence. It begins with fundamental mathematical concepts related to linear algebra and probability theory, and then introduces key machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning.  

The course covers fundamental concepts and principles of machine learning algorithms, analysis of real-world data using machine learning techniques and programming tools, and development of machine learning solutions for real-world problems in various domains. 

Topics include Parametric Density Estimation, Linear Regression, Classification and Logistic Regression, Generative Learning Algorithm, Deep Learning and Neural Networks, Generalization and Regularization, Clustering and K-means Algorithm, Dimensionality Reduction, Generative Models, and Markov Decision Process and Reinforcement Learning. 

Prerequisites: Probability, Linear Algebra, Python