Seoul National University

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.   

This course cultivates entrepreneurship through understanding the current status and trends of the semiconductor industry and the experiences of company founders. In the class, students meet representatives of recently established semiconductor companies, hear about the startup motivation, business model, and future prospects, and share understanding of the overall semiconductor industry and various experiences related to technology-based startups through a Q&A session with the semiconductor CEO. 

This course covers estimating relationships between economic variables associated with agricultural situations. Students are enabled to understand the general concepts about model identification, estimation, forecasting, and policy analysis. Students learn simple regression, multiple regression, and time series analysis. Prerequisite: Principles of Economy, Statistics, Mathematics for Economic Analysis. 

This class is designed for international students studying at SNU, in particular those students who may not have had the opportunity yet to study the language or culture of Korea. It provides a general introduction to the Korean language and important aspects of Korean culture, both traditional and modern. Topics include an outline of the Korean language with honorific forms, Korean history, nature, economy, and society. The course also covers Korean art, music, literature and philosophy as well as problems concerning the traditional culture such as family, relatives, wedding, funeral ceremony, folk's belief, shamanism, seasonal rite and customs. The class is conducted in English, including all instruction, discussion, and assignments. 

This course focuses on the food industry and studies the success and failure cases of marketing and information management in fields such as agriculture, food service, bio industry, and distribution industry, and discusses how to apply them for the development of our food industry. Through case analysis, students will acquire various practical knowledge on how business activities in these industries are developed from the perspective of marketing and information management, and how to solve problems using methods and frameworks. Students who successfully complete this course will have basic skills as management consultants in the field of food and bio business. 

This course introduces population geography to undergraduate students and focuses on the causes and consequences of population change. It enables students to understand demographic dynamics brought about by birth, death, and mobility. The course examines the tension between how demographic knowledge (and in particular, demographic categories) has been constructed and how such categories are used. The course pays special attention to the spatial mobility of human beings as the increase in human mobility receives increasing attention from both academia and policy-making. 

This course covers the concepts, paradigms, and explanations needed to become effective practitioners in culturally, racially, and linguistically diverse classrooms and schools. An important of goal of this course is to help future educators attain a sophisticated understanding of the concept of culture and to view race/ethnicity, gender, and class as interacting concepts rather than as separate and distinct. As a result, intersectionality, i.e., how race/ethnicity, gender and class are fluid variables that interact in complex ways is an overarching concept in this course. In doing so, the course integrates the content of Sustainable Development Goals (SDGs) to provide students an opportunity to explore issues of inequalities and injustices related to race/ethnicity, gender, and class within their communities from a global perspective.  

The course collaborates with National Taiwan Normal University (NTNU) through online interactions and Project-Based Learning (PBL) to facilitate participants’ understanding of local issues and current circumstances pertaining to race/ethnicity, gender, and social class in South Korea and Taiwan. Through PBL focusing on student-selected topics, participants from both societies will actively engage in intercultural collaboration and knowledge exchange, promoting social inclusivity and advancing SDGs grounded in principles of multicultural education and sustainable development. 

This course covers programming using data programming language on an introductory yet rigorous basis for highly motivated students with little or no prior experience in programming. The course focuses on collecting and analyzing data, as well as the grammar of the data programming language and provides an in-depth look at big data analysis.  

Modern scientific, engineering, and business applications are increasingly dependent on data, yet traditional data analysis technologies were not designed for the complexity of big data. Big data analysis has emerged as a new, exciting, and fast-paced discipline that explores novel statistical and implementation challenges that emerge in collecting, processing, storing, and extracting knowledge from big data.  

Students learn how to collect, process, and analyze large amounts of data by combining data analysis technology and artificial intelligence technology.  Students will use Python as a powerful tool for data management and analysis and analyze structured and unstructured data using LLMs for natural language processing, text analysis, and graph-based multi-agent systems.  In addition, data management and system design techniques using RAG (Retrieval-Augmented Page 1 of 9 Generation) and APIs that support prompt engineering techniques and AI-based decision-making will also be covered. 

This is a useful course for students who want to combine and utilize data analysis and artificial intelligence technology in various fields such as information technology (IT), business analytics, marketing, and strategic planning. 

Prerequisites:  Basic knowledge of the system configuration of the operating system (Windows, macOS, etc.) (e.g., setting environment variables, etc.).  Basic knowledge of the Python programming language