Yonsei University

This course examines international nuclear non-proliferation regimes and geopolitical situations as an important part in international peace and security. Via case studies, the course explores the history of nuclear weapon development and the establishment of nuclear non-proliferation regimes as well as nuclear disarmament agreements between the United States and Russia (formerly the Soviet Union) and the UN’s efforts to ban nuclear weapons. 

The course further addresses the history of nuclear weapon development, use, and related, resultant negotiations, treaties, and subsequent political impact upon and by various governing bodies throughout the world. The course situates and reviews cases in the geopolitical context: nuclear development and possession in India, Pakistan, and Israel; the Cuban Missile Crisis; and Iranian nuclear development.  

In addition, the possibility of further nuclear proliferation by Saudi Arabia, Egypt, Japan and Taiwan is discussed in relation to geopolitical interests, and North Korea's nuclear tests and South Korea’s nuclear option are debated in the context of North Korea’s nuclear threat and geopolitical strategy. 

This course explores generative artificial intelligence (GAI) and its applications. Students gain a comprehensive understanding of generative models, including deep learning architecture, and probabilistic models. The course covers theoretical foundations and practical implementations of generative AI algorithms. Students also engage in hands-on projects to apply generative AI methods. Topics include introduction to generative AI (overview of generative modeling, brief history of GAI, applications of GAI), probability theory and information theory, parameters estimation, latent variable models, variational inference (introduction), variational autoencoders (VAEs) - autoencoders - variational autoencoders (VAE) - conditional VAE - VQ-VAE v1, v2, generative adversarial networks (GANs) - introduction to GANs - GAN training, issues and solution - generative model evaluation, GAN variants: DCGAN, CGAN, WGAN, ProGAN and Style-GAN, GAN applications: image manipulation and editing, diffusion-based generative models - DDPM - DDIM, diffusion-based generative models - classifier guidance DMs - classifier-free guidance DMs - cascaded DMs - latent DMs, autoregressive generative models - MADE, PixelNN, language generative models - Transformer - GPT family, multi-modal generative models - DALL-E (DALL-E 2 and DALL-E 3) - stable diffusion, flow-based generative models - RealNVP, GLOW.

Prerequisite: Solid understanding of machine learning and deep learning principles - Proficiency in programming - Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch)

The contemporary world is facing many challenges in promoting peace, justice and reconciliation, ranging from armed conflicts to social inequality, from environmental degradation to interfaith tensions. These complex challenges continue to afflict many parts of the globe. In this context, some fundamental questions may be asked: how do we define life?; What does it mean to live a life of integrity?; How does my life relate to just, sustainable and inclusive peace?; How do the ideas for making the world more just and peaceful shape our own lives and careers of purpose and vice versa? Seeking to explore these questions deeply, this course presents foundational theories behind peace and social justice and applies these concepts to specific fields of inquiry and practice, including: colonization, violence, oppression, racism, sexism, human trafficking, poverty, climate change and complex issues of peacebuilding, humanitarian aid and development. Various strategies and attempts to create social change for the greater good through different individual and organizational platforms are analyzed and assessed too. Throughout the course, students gain an understanding of the strengths and constraints on theory and practice in the context of the creation of a culture of “human flourishing”, particularly in post-conflict societies, and engage in a variety of topics with self-reflective approach.

This course explores the significant achievements of behavioral economics, focusing on theoretical and empirical evidence. Unlike traditional economics, which assumes human rationality and standard preferences to understand decision-making and behavioral changes, behavioral economics expands human decision-making models based on new insights from psychology and other fields and introduces the concept of policy design and evaluation based upon insights into human behavioral change. Students will gain a thorough understanding of the fundamental concepts of behavioral economics; be able to critically evaluate the traditional economic theories on human rationality; and be able to compare and analyze the usefulness and policy implications of behavioral economics with those of traditional economics. 

Prerequisites: Microeconomics, Basic Calculus 

This course explores the transformative role of new information technologies (IT) and artificial intelligence (AI) in reshaping businesses. Its goal is to equip students with an in-depth understanding of essential economic principles that are crucial for operating and excelling in IT-enabled and/or AI-driven enterprises, while also highlighting the economic ramifications of IT and AI at various levels - including individual firms, broader markets, and society as a whole.  

Students will delve into competitive market analysis and examine IT-specific economic challenges related to pricing, bundling, information asymmetry, and uncertainty. In addition, the module covers AI-centric topics such as automated decision-making and the broader economic effects of AI technologies. Key economic issues associated with IT (such as competitive markets, pricing strategies, and bundling) and foundational concepts like user lock-in, switching costs, and the network effect are discussed. Participants also gain insights into the workings of AI-enabled businesses and address AI-specific economic concerns, including automated decisions, algorithmic bias, and the influence of AI technologies on the job market. 

This course examines linguistic phenomena relating to the structure of language and how those phenomena are formulated and explained under the framework of so-called Generative Grammar.  

Course objectives include: i) to understand what is meant by the structure of language, ii) to examine linguistic facts discerned to be structural, iii) to appreciate conceptual/theoretical necessities to account for them (e.g., diverse developments from Generative to Minimalism), iv) to have a grasp of the idea of universal grammar. 

Topics include linguistics and syntax, ingredients of structure: linearity and hierarchy, syntactic categories, words to phrases, two kinds of merge: substitution and adjunction (external or internal), introduction to P-markers, various structural relations (Binding Theory), complement vs. adjunct (and specifier), covert elements: trace vs. empty categories (PRO/pro), movement and interpretation: 1. grammatical functions 2. thematic roles 3. displacement (overt movement vs. covert movement like QR), and transformation: substitution and ellipsis. 

This class provides fundamental understanding of energy conversion by use of power electronic devices. Students are expected to perform analysis and synthesis of power electronic systems after this course. Expected outcome includes: 1. Demonstrate the ability to analyze switching power converters in steady state using circuit averaging and determine DC voltages and currents 2. Be able to sketch current and voltage waveforms in a converter in steady state 3. Demonstrate the ability to size passive filtering components in converters such as inductors and capacitors to obtain a desired ripple performance 4. Demonstrate the ability to derive small-signal linearized models for switching converters 5. Demonstrate an understanding of the effects of negative feedback on converter operation 6. Demonstrate the ability to simulate switching converter using both switching models and averaged models via PSCPICE. 

Prerequisite: EEE2010 (Basic Circuit Theory) 

This course covers 600 years of Seoul history, the Chosun (1394-1897), Taehan Empire (1897-1919), and the Japanese Colonial Period (1910-1945). As of 2020, Seoul has a population of 9.97 million and is considered one of the top ten metropolitan economies in the world. By exploring the history of Seoul, students gain an understanding of the history of one of the oldest cities in the world and also the dynamic history of modern Korea.  By the end of the course students should be able to understand early modern Korean history and its significant events. In addition, students will understand how modern-day Seoul was established via its 600 year history.

This course provides upper-level undergraduate students an holistic overview of physical processes that develop alluvial, eolian, lake, delta, shoreline, and shallow-marine, tidal depositional systems. The focus of this class ranges from applications of the basic principles i.e., sediment transport and depositional mechanics to stratigraphy and basin interpretation in various depositional environments. Topics include Interpreting sedimentary successions, Ichnology and facies models, Siliciclastic facies models, Glacial deposits, Alluvial deposits, Eolian systems, Wave-and storm-dominated shoreline and shallow-marine systems, Tidal depositional systems, Deltas, Transgressive wave-dominated coasts, Deep-marine sediments and sedimentary systems, and Lakes.

Prerequisite: Sedimentary Petrology

This class is designed to equip students with the fundamental principles of statistics commonly used in communication research. This course is the first in a sequence of graduate methodology classes required of all students enrolled in the M.A. or Ph.D. program in Communication. Students acquire working familiarity with the basic principles and theory behind descriptive and inferential statistics. By the end of the semester students understand the difference between descriptive and inferential statistics, understand the logic of null hypothesis significance testing, and be able to conduct basic statistical analyses (including t-tests, a single-factor ANOVA, correlation, regression, and chi-square) using commonly used statistical software such as R. Students who complete this course are able to read and understand empirical research, analyze data from their research projects, and report results in accordance with the APA standards. Topics include Basic Concepts and Vocabulary, Introduction to R, Probability, Independence, and the Normal Distribution, Hypothesis Testing Concepts and Applications, Factorial ANOVA, Correlation & Chi-square, and Regression fundamentals.