Seoul National University

This introductory course covers mathematical topics closely related to computer science. Topics include: logic, sets, functions, relations, countability, combinatorics, proof techniques, mathematical induction, recursion, recurrence relations, graph theory, and number theory. The course emphasizes the context and applications of these concepts within computer science. Prerequisites: No prior programming experience is assumed. 

This course examines Existentialism and Phenomenology in terms of their unique and considerable contributions to the Western, and particularly French, aesthetic tradition. Students examine views on art by some of the best-known modern theorists to gain understanding of the philosophical issues motivating French aesthetic thought at the end of the 19th and beginning of the 20th Centuries. The course then covers a shift from a broadly existentialist view of literature to one influenced by the growing structuralist movement and reviews philosophical investigations of the arts in relation to theories of perception.  

This special topics data sciences course covers up-to-date research trends in prompt engineering and prompt engineering interactions with large scale language modeling. The course examines how prompt engineering significantly impacts the effectiveness of LLM-based applications and interactions with generative AI. 

Academic researchers, industry vendors, and practitioners have proposed many practical techniques and guidelines for building LLMs or applications on LLMs. In this course, students review concepts and techniques that can be used to guide the model in how to behave in a way that is aligned with users' preferences or perform a specific task. 

Topics include basic concepts of LLMs, Foundation model vs custom model, Fine tuning vs prompt tuning, Methods of prompt engineering, Agentic workflow, Integrating local preparatory knowledge bases, and more. 

This course introduces an overview of the cultural tradition of East Asia by examining classical Chinese narratives translated into English. It explores how these narratives depict the essence of humanity and the world, discussing their influence on modern East Asian culture. Through the course, students identify the cultural characteristics inherent in East Asian civilization and develop a critical understanding of its contemporary discourses. 

Student will be able to: 1. Understand the cultural concepts that underlie the individual, the family, and the state in East Asia 2. Learn the historical development of China, Japan, and Korea and their relationships with each other 3. Practice reading East Asian texts in their own literary tradition and relating them to cultural contexts 

This course theoretically examines the macroscopic role that entrepreneurs and entrepreneurship play in the market economy and examines management theories about individual and organizational-level phenomena that affect entrepreneurship, for example via management case studies. The scope of this class is not a simple livelihood-type entrepreneurship, but opportunity-capturing entrepreneurship that creates new market value. Topics include History of the Startup Ecosystem in Korea, Entrepreneurship vs Management, Opportunity recognition, Numbers and Venture Capitalists, Business Model and Competitor Analysis, Consumer Behavior, Business Models Topology and Big Data, and more. 

This course explores the underlying principles of several cutting-edge topics in machine learning and deep learning, including adversarial attacks, deep metric learning, generative models, information theory, and reinforcement learning.  

In addition, the course examines the end-to-end construction of modern large language models and practices core concepts by implementing them. Students engage in coding assignments and team projects using GPU-enabled computer servers to test original ideas. 

Topics include concepts and history of deep learning, backpropagation techniques such as stochastic gradient descent, initialization techniques, regularization techniques such as drop out, convolutional neural networks (CNN), CNN architectures, visualization of CNN, recurrent neural networks (RNN), RNN applications, and other applications including reinforced learning. 

To emphasize practical skills to implement deep learning algorithms, programming-related lectures and lab sessions are included. The most important/popular language, Python, will be covered and a Python math library called Numpy is also taught with lab sessions. Advanced deep learning algorithms are implemented in Tensorflow library, which is introduced as well including relevant lab sessions 

This course compares communication phenomena of East Asian societies using student-led international discussions, group studies, and special lectures. Topics include understanding of Chinese, Japanese and Korean media, as well as comparing western and eastern media characteristics. 

This course challenges the limitations of border-based thinking about and explores diverse aspects of (East) Asian society, particularly Korea, Japan, China, and beyond, through the layers of histories, networks, and complex sociotechnical entanglements. Drawing from the methods and theories in Communication and Media Studies, Cultural Studies, Asian/Global Studies, and Science and Technology Studies (STS), the course takes a critical, historically informed, and locally grounded approach to examine both the material and immaterial layers constituting the location in question. Through this course, students reflect on their experiences and perceptions of Asia, practice synthesizing theory with practice, and produce contextualized knowledge about Asia.    

This course covers concepts and characteristics required to build and operate a smart factory to improve manufacturing competitiveness. Students first learn about the composition and functions of the 4th Industrial Revolution and smart factories, then examine the general concept of factory automation and learn about the basic components of factory automation such as control systems and PLCs.  The course covers functions of corporate information systems such as ERP, MES, APS, and PLM, and concepts of smart factory digital platforms and big data-based decision-making required for intelligent factory operation. 

This course covers the application of management science methodology to various decision-making problems in the agricultural and food industry, models them mathematically, and derives optimal strategies. The course enables students to actually apply and discuss management science methodology to corporate cases related to the agricultural and food industry. Specifically, in this course, students acquire theoretical models for linear programming, network models, integer programming, nonlinear programming, multi-objective decision-making, decision-making under uncertainty, simulation, and queueing, and learn how to analyze them. In addition, it enables students to apply management science methodology to topics such as agricultural cultivation, food distribution and supply chain, and agricultural and food production planning, and derive and interpret results. 

This course provides an opportunity to learn through practice of combined fundamental mathematics and programming to understand machine learning. The course operates as micro-learning that allows students to learn the necessary unit concept of mathematics and learn through programming exercises immediately. This course covers the essential requirements for machine learning such as algebra, calculus, linear algebra, and geometry. The programming language used in this course is Python. This course is mainly targeted for undergraduate students with advanced high-school level mathematics but with no background in programming. Some basic machine learning algorithms will be introduced to show the application of mathematics in practice. Finally, some advanced learning algorithms and important topics will be reviewed.