Yonsei University

This course primarily focuses on economic analysis in terms of welfare. Topics include how to evaluate market allocations based on efficiency, how to achieve efficient allocations through the market, and when the market fails in achieving efficient allocations.  

Students will study market structures besides the competitive market such as standard monopoly (uniform pricing), monopoly behavior (price discrimination) and oligopoly (basic concepts in game theory are also covered).  

Additionally, the course will consider exchange, production, welfare, social choice (e.g., an investigation of voting rules), and externalities (If time allows).  

This course emphasizes the development of microeconomic models to analyze economic decision-making of agents and provides students with the basic toolkit of microeconomic theory in preparation for advanced further coursework.  

Prerequisites: Principles of Microeconomics, Basic calculus 

This course examines major topics in pattern recognition, particularly aspects of classification and decision. Students will gain effective pattern recognition tools with which to analyze the often vast amounts of diverse data in research applications. 

Topics include introduction to pattern recognition - machine perception - PR systems and design cycle, Bayesian decision theory for continuous features - Bayes Decision Rule - minimum-error-rate classification - classifiers, normal density, discriminant functions and discrete Bayesian decision theory - discriminant functions for the normal density - error probabilities and integrals - Bayes Decision Theory for discrete features, maximum-likelihood and Bayesian parameter estimation - Bayesian parameter estimation: Gaussian case - Bayesian parameter estimation: general theory - HMM, nonparametric techniques - density estimation - Parzen windows - nearest neighbor estimation (NN, k-NN) - fuzzy classification, linear discriminant functions i - linear discriminant functions and decision surfaces - generalized linear discriminant functions - minimizing the perceptron criterion function, relaxation procedures, linear discriminant functions ii - minimum square-error procedures - relation to Fishers linear discriminant - the Widrow-Hoff and Ho-Kashyap procedures - multicategory generalizations - ridge regression and its dual form [2] - classification error based method [2], model assessment and performance evaluation - bias, variance and model complexity [2] - model assessment and selection 2] - confusion matrix, error rates, and ROC [2] - statistical inference [2] - statistical errors [2], dimension reduction and feature extraction - principal component analysis - Fisher linear discriminant - nonlinear projections, support vector machines - introduction - SVM for pattern recognition [2] - linear support vector machines [2] - nonlinear support vector machines [2], multilayer neural networks - introduction - feedforward operation and classification - backpropagation algorithm - some issues in training neural networks - key ideas in classification, introduction to deep learning networks - convolutional neural networks (CNN) - autoencoders - deep belief networks - deep reinforcement learning - generative adversarial networks (GAN), algorithm-independent machine learning - introduction - bias and variance - resampling for classifier design - estimating and comparing classifiers - combining classifiers, unsupervised learning and clustering - mixture densities and identifiability - maximum-likelihood estimates - application to normal mixtures. 

Prerequisites: Linear Algebra, Probability, MATLAB, Python, or C-Programming Skills 

This course encompasses analyses of the psychological impact of media content and presentation. The courses provides an understanding of how individuals process media contents as well as how the media affects individuals’ knowledge, attitudes, and behaviors. A variety of topics such as the psychological processing of information, media violence, sexual content, stereotyping, and the effects of new communication technologies are covered. 

This course provides an in-depth examination of the intersection between trade law and diplomacy in the context of international trade relations. Students will gain an understanding of real-world trade issues and will investigate the role of diplomacy in trade relations as well as the practice of trade laws focusing on the World Trade Organization and its dispute settlement procedure. 

The course provides background case analysis of the previous WTO disputes and covers current and emerging trade issues such as US-China trade dispute, Indo-Pacific Economic Framework, Tech Diplomacy, and AI-related trade issues.  

Professor Sangsoo Yoon was a career diplomat working for the Korean Foreign Ministry and his previous post was Consul General of the Republic of Korea in San Francisco, USA. During his diplomatic career, Professor Yoon has been heavily involved in multilateral trade negotiation in the World Trade Organization and has unique expertise in WTO dispute settlement procedures.  Professor Yoon will share his first-hand knowledge on trade law and diplomacy and foster dialogue on current trade-related diplomatic issues. 

This class is an intermediate writing course tailored to exchange students. The goal of this course is to enhance intermediate-level Korean writing communication skills by experiencing Korean writing communication in various contexts and genres. To this end, this course deals with exchange students' school life, making various friends, exploring Korean life, their interests, situations where exchange students are likely to encounter, and their careers after graduation.

This course explores the basics of opto-electronics and photonics, which has many applications areas in information and communication technologies. By the end of the semester, students should have basic knowledge of (1) what light is, (2) how the basic property of light can be modeled, and (3) how light can be used for various applications. Topics include basics of electromagnetism, maxwell's equations, plane-wave solutions, polarization, EM waves in conductor, total internal reflection, interference, light incident on conductors, light incident on dielectric interface, multiple dielectric interface, interferometers, diffraction, metallic waveguides, dielectric waveguides, 2-D dielectric waveguides, optical fiber, waveguide devices, photons, interaction between light and matter, optical amplifiers, semiconductors, semiconductor lasers, single mode lasers, and photodetectors.

Prerequisite: Basic knowledge in electromagnetism

This course provides an in-depth exploration of building science topics related to sustainable buildings. Through a combination of lectures, workshops, and hands-on projects, students learn the fundamental scientific principles underlying these phenomena and gain practical experience with technologies and analytical techniques for designing comfortable and energy-efficient indoor environments. The course covers a broad range of topics, including climate analysis, solar energy, heat transfer, natural ventilation, HVAC systems, renewable energy, acoustics, biophilic design, landscape design, and water systems. Students apply these principles in real-world scenarios, learning to integrate energy, light, and sound considerations into architectural design to enhance building performance and occupant comfort. Topics include Introduction to Sustainable Buildings, Understanding Climate - Methods for Environmental Analysis, Understanding Comfort - Psychrometrics and Bioclimatic Chart, Solar Energy and Daylighting, Material and Building Heat Transfer, Wind and Natural Ventilation, Building Performance Simulation, HVAC and Renewable Energy, Indoor Environmental Quality, Acoustics and Biophilic Design, and Landscape Design and Water system.

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.