Yonsei University

This course covers the principles of relationship marketing. Students are introduced to strategic and tactical issues involved in building and managing relationships with customers. The course also deals with analytical methods for identifying customer needs, calculating customer lifetime value, making targeting decisions, and evaluating the impacts of marketing activities. Emphasis is placed on the implementation of the methods using software tools. Topics include Marketing math, Analyzing customer data, Identifying customer needs & segmentation, Evaluating the impacts of marketing activities, Utilizing transaction data, Making targeting decisions, Customer retention, Customer lifetime value, and Relationship marketing in digital environments.

This course explores various aspects of media ethics through the study and discussion of recent case studies, helping students build a universal foundation for ethical judgment as they encounter ethical dilemmas as media professionals or consumers. This course aims to help student better understand the ways in which media ethics affects our daily lives. Students develop their presentation and critical thinking skills through class activities such as group discussions. This course aims to engage students in some of the major issues in media ethics. Topics include Advertising Ethics, Public Relations Ethics, Journalism Ethics, Social Media Ethics, and AI and Robot Ethics. 

This course covers the properties and applications of maximum likelihood estimation (MLE), including its consistency, asymptotic normality, and efficiency, and applies these concepts to real-world statistical problems. Students analyze hypothesis testing frameworks, covering the Neyman-Pearson lemma, likelihood ratio tests, and their implementation for single-parameter and multi-parameter models and study the principles of sufficiency and completeness in statistical inference and use the factorization theorem to identify sufficient statistics for various distributions. Students also explore confidence interval construction methods, focusing on pivotal quantities, and evaluate their properties such as coverage probability and efficiency and we apply advanced inferential techniques to solve problems involving exponential families, sequential analysis, and decision-theoretic approaches, linking theory to practice. 

Prerequisites: Mathematical Statistics I, Linear Algebra, Calculus 

This course introduces the characteristic of the materials of engineering and mechanical action of a material. The course emphasizes the need to understand of all kinds of the materials, the mechanical property of the engineering materials as well as to acquire the knowledge for selecting the required material for the particular engineering design. It is imperative to discover the characteristics of engineering materials and their mechanical actions so as understand how each material will perform in practice, not just in theory. Students attempt to determine the optimal mechanical action of materials. Text: William D. Callister and David G. Rethwisch, FUNDAMENTALS OF MATERIALS SCIENCE AND ENGINEERING. Assessment: exam, assignments, project and attendance.

This course examines myths in relation to culture and surveys representative theories of mythology. Students read classical myths, explore the cultural elements that gave rise to particular myths, and learn to apply this knowledge in an analysis of "modern" myths, beginning with folktales and local legends.   

Topics include What is myth and why is it relevant?, The Dawn, The Olympian Gods, Gods and Human Beings, Death and Rebirth, Demeter, Persephone, Dionysus, Apollo and Artemis, Aphrodite, Myth and History, Heroes and demigods, Theseus and the Minotaur, Herakles, The Trojan War, Justice, vengeance, and punishment, The Tragic House of Atreus, Fate vs. human will, Oedipus, Medieval myths, Faust and Satan, and Folktales. 

Sustainable marketing involves the creation and delivery of value to customers in ways that honor and improve both environmental and societal wellbeing. Through lectures and case studies, this course covers the principles of sustainable marketing across various industries, such as apparel, food, automotive, and technology, and provides an understanding of the sustainable consumer.  

Students learn to develop sustainable marketing strategies and explore how traditional marketing principles are being reshaped by growing sustainability concerns.  

The course defines sustainable marketing and links it to related business concepts like corporate social responsibility and delves into the "triple bottom line" framework, which encompasses economic performance, environmental impact, and social impact. Utilizing real-world examples, the course examines the buy-one-give-one business model through the lens of Bombas, reviews corporate sustainability strategies, and discusses how Allbirds adapts its approach to maintain competitiveness. The courses also covers the characteristics, psychology, and behaviors of sustainable consumers.  

Students are introduced to the Intention-Behavior gap, which highlights the difference between consumers' sustainable attitudes and their actual behaviors. Through the SHIFT framework, students investigate ways to bridge this gap by addressing key factors such as social influence, habit formation, individual identity, feelings and cognition, and tangibility. Additionally, students analyze sustainable consumer trends, including voluntary simplicity, vintage fashion, and sustainable luxury.  

A detailed examination of Norlha, a luxury yak wool textile enterprise on the Tibetan Plateau, provides a case study in sustainable luxury. 

This class then covers the essential steps for creating a sustainable marketing plan, discussing product development and marketing sustainable innovations, and applying these concepts through the Aleph Farms case.  

Next, students learn how to use life cycle analysis to assess the social and ecological impacts of sustainable products and explore issues related to sustainable supply chain management and the management of re-commerce platforms within the circular economy. Finally, we study how to design effective communication campaigns for sustainability goals, illustrated by the General Motors case on electric vehicles. 

This topical course covers Twentieth-Century American Crime Fiction. This is one of the most popular genres worldwide—especially in the United States. Despite its enormous impact on popular culture, this genre remains one of the least developed areas in terms of recognized literary value, which makes it a fascinating subject for study. In this course, we read a variety of crime fiction works that have captured the American imagination throughout the twentieth century, and we will: 1. Examine each text in detail, discussing its aesthetic, stylistic, and thematic qualities; 2. Explore how the genre has evolved in conversation with popular culture, in order to better understand the sociocultural significance of crime fiction in America; 3. Use crime fiction as a lens through which to critically engage with existing theories of genre in literary criticism. 

This course provides a comprehensive introduction to both the practical and rigorous foundations of software engineering as well as both soft and hard skills. With them, students learn how to design, develop, test, verify, and maintain high-quality software systems.  

Topics include software development life cycles, design patterns, testing and coverage, code quality practices such as code reviews and coding style guides, and formal verification techniques.  

In this course, students engage in software engineering by applying coding, refactoring, and testing techniques to support continuous development and maintainability in real-world projects. Students also analyze given software designs or code using design patterns, testing strategies, and code quality standards. We also evaluate the strengths and limitations of various development methodologies and design alternatives to determine the most suitable approach for a given context. We design and implement tools or procedures that verify software correctness using logic-based reasoning and formal methods and build maintainable, high-quality software systems. 

Prerequisites: CAS1102 (Object-Oriented Programming), CAS2103 (Data Structure); students should be familiar with object-oriented languages including C++, Python, etc. Students should be familiar with implementing data structures and be able to analyze pros and cons of several data structures. Students should have a Github account.  

This course provides students with an opportunity to become a sophisticated, critical, and creative user of generative artificial intelligence (GenAI).  

Through this course students gain a practical mastery of current AI tools, but are also challenged and prepared to move beyond basic AI use to develop skills in prompt engineering, tool comparison, and critical output evaluation and to design and implement effective AI-powered workflows to solve complex academic and professional tasks related to research, writing, data analysis, and communication. 

Students also critically analyze the ethical responsibilities of AI use (bias, privacy, integrity) and articulate the broader philosophical implications for your work, your mind, and your identity.  

Topics include Introduction to the course's Syllabus and lab-based philosophy; What is Generative AI?; Understanding our own "mental models" of AI; The principles of effective prompt engineering; The landscape of major LLMs (open vs. closed source); Retrieval-Augmented Generation (RAG) as a tool against hallucination; Overview of specialized AI tools for academic reading and writing; AI capabilities beyond text: Vision, Voice, and Code; Integrating multiple AI tools into a single workflow; Understanding AI "agents," APIs, and the role of local LLMs; The FOCUS Method for AI-assisted research; Finding and organizing information effectively; AI as a writing partner and coding assistant; Ethical considerations in AI-assisted writing; Designing AI-powered workflows for personal productivity, email management, and lifelong learning; Key limitations of AI (bias, privacy, hallucinations); Principles of ethical AI use; University policies on academic integrity; The broader societal impact of AI on science, equity, and the future of work; and The nature of intelligence, creativity, and consciousness in the age of AI.